Defining a collection of media content items for a relevant interest

ABSTRACT

Systems, methods, and computer-readable media for defining a collection of media content items of a media library for a relevant interest are provided.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.15/844,409, filed Dec. 15, 2017 (now U.S. Pat. No. 10,839,002), whichclaims the benefit of prior filed U.S. Provisional Patent ApplicationNo. 62/514,910, filed Jun. 4, 2017, each of which is hereby incorporatedby reference herein in its entirety.

TECHNICAL FIELD

This disclosure relates to defining a collection of media content itemsfor a relevant interest with an electronic device.

BACKGROUND OF THE DISCLOSURE

A system may be provided for managing a library of media items. Often,however, management of such a library may fail to identify certain mediaitems as being indicative of a relevant interest of a user.

SUMMARY OF THE DISCLOSURE

This document describes systems, methods, and computer-readable mediafor defining a collection of media content items of a media library fora relevant interest with an electronic device.

For example, a non-transitory machine readable medium storing a programfor execution by at least one processing unit of a device may beprovided, the program for managing a media library, the programincluding sets of instructions for accessing a plurality of mediacontent items (MCIs) of the media library and metadata associated withthe media library, wherein the metadata defines a plurality of moments,each moment of the plurality of moments is associated with a subset ofMCIs of the plurality of MCIs, and each MCI of the subset of MCIs thatis associated with a particular moment is associated with geographicalmetadata indicative of a geographic location within a particulargeographic range associated with the particular moment and temporalmetadata indicative of a time within a particular time range associatedwith the particular moment, analyzing the plurality of MCIs and themetadata, wherein the analyzing includes identifying a plurality offirst person-residence moments from the plurality of moments, whereineach first person-residence moment of the plurality of firstperson-residence moments is a moment of the plurality of moments that isassociated with both a first person and a residence of the first person,and identifying an interest that is associated with each one of a firstnumber of first person-residence moments of the plurality of firstperson-residence moments, wherein the first number is greater than athreshold value, and defining a collection of MCIs of the plurality ofMCIs, wherein each MCI of the collection of MCIs is associated with amoment of the plurality of moments that is associated with both thefirst person and the interest.

As another example, a method of managing a media library with acomputing system may include accessing, with the computing system, aplurality of media content items (MCIs) of the media library andmetadata associated with the media library, wherein the metadata definesa plurality of moments, each moment of the plurality of moments isassociated with a subset of MCIs of the plurality of MCIs, and each MCIof the subset of MCIs that is associated with a particular moment isassociated with temporal metadata indicative of a time within aparticular time range associated with the particular moment, analyzing,with the computing system, the plurality of MCIs and the metadata,wherein the analyzing includes identifying at least one person-residencemoment from the plurality of moments, wherein each person-residencemoment of the at least one person-residence moment is a moment of theplurality of moments that is associated with a location intimatelyassociated with a person identity, and identifying an interest that isassociated with at least one of the at least one person-residencemoment, and defining, with the computing system, a collection of MCIs ofthe plurality of MCIs, wherein each MCI of the collection of MCIs isassociated with the identified interest.

As yet another example, a method of managing a media library with acomputing system may include accessing, with the computing system, aplurality of media content items (MCIs) of the media library,identifying, with the computing system, at least a threshold number ofMCIs of the plurality of MCIs that are associated with both a particularperson entity and a particular interest, and defining, with thecomputing system, a collection of MCIs of the plurality of MCIs, whereineach MCI of the collection of MCIs is associated with the particularinterest.

This Summary is provided only to summarize some example embodiments, soas to provide a basic understanding of some aspects of the subjectmatter described in this document. Accordingly, it will be appreciatedthat the features described in this Summary are only examples and shouldnot be construed to narrow the scope or spirit of the subject matterdescribed herein in any way. Unless otherwise stated, features describedin the context of one example may be combined or used with featuresdescribed in the context of one or more other examples. Other features,aspects, and advantages of the subject matter described herein willbecome apparent from the following Detailed Description, Figures, andClaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The discussion below makes reference to the following drawings, in whichlike reference characters may refer to like parts throughout, and inwhich:

FIG. 1 is a schematic view of an illustrative system for defining acollection of media content items of a media library for a relevantinterest;

FIG. 2 is a front view of an illustrative example of an electronicdevice in the system of FIG. 1 ;

FIG. 3 is an exemplary block diagram of at least a portion of a libraryof media content pieces of the system of FIGS. 1 and 2 ;

FIG. 4 is an exemplary block diagram of a portion of an illustrativemetadata network of the system of FIGS. 1 and 2 ;

FIG. 5 is a schematic view of an illustrative portion of the system ofFIGS. 1-4 ; and

FIGS. 6-9 are flowcharts of illustrative processes for managing a medialibrary.

DETAILED DESCRIPTION OF THE DISCLOSURE

Systems, methods, and computer-readable media may be provided to definea collection of media content items of a media library for a relevantinterest. For example, a combination of a particular interest and aparticular person identity may be identified based on analysis of amedia library of media content items (“MCIs”) in order to determinewhether the particular interest is relevant to the person identity. Forexample, a location may be determined to be intimately associated withthe person identity (e.g., a home or place of business of the personidentity), and then it may be determined whether the particular interestmay be associated with a particular threshold amount of MCIs (or momentsof MCIs) also associated with any such intimate location. Some or allMCIs determined to be associated with a relevant interest of a personidentity may be used to define an MCI collection for any suitablepurpose (e.g., for defining an easily accessible album of such MCIs orfor generating a composite presentation of the MCIs for user enjoyment).

FIG. 1 is a schematic view of an illustrative system 1 that may includeat least one of electronic device 100 and remote server 50 for defininga collection of media content items for a relevant interest inaccordance with some embodiments. Electronic device 100 can include, butis not limited to, a media player (e.g., an iPod™ available by AppleInc. of Cupertino, Calif.), video player, still image player, gameplayer, other media player, music recorder, movie or video camera orrecorder, still camera, other media recorder, radio, medical equipment,domestic appliance, transportation vehicle instrument, musicalinstrument, calculator, cellular telephone (e.g., an iPhone™ availableby Apple Inc.), other wireless communication device, personal digitalassistant, remote control, pager, computer (e.g., a desktop, laptop,tablet (e.g., an iPad™ available by Apple Inc.), server, etc.), monitor,television, stereo equipment, set up box, set-top box, boom box, modem,router, printer, watch, biometric monitor, or any combination thereof.In some embodiments, electronic device 100 may perform a single function(e.g., a device dedicated to defining a collection of media contentitems for a relevant interest) and, in other embodiments, electronicdevice 100 may perform multiple functions (e.g., a device that manages amedia library, plays music, and receives and transmits telephone calls).Electronic device 100 may be any portable, mobile, hand-held, orminiature electronic device that may be configured to define acollection of media content items for a relevant interest wherever auser travels. Some miniature electronic devices may have a form factorthat is smaller than that of hand-held electronic devices, such as aniPod™. Illustrative miniature electronic devices can be integrated intovarious objects that may include, but are not limited to, watches (e.g.,an Apple Watch™ available by Apple Inc.), rings, necklaces, belts,accessories for belts, headsets, accessories for shoes, virtual realitydevices, glasses, other wearable electronics, accessories for sportingequipment, accessories for fitness equipment, key chains, or anycombination thereof. Alternatively, electronic device 100 may not beportable at all, but may instead be generally stationary.

As shown in FIG. 1 , for example, electronic device 100 may includeprocessing circuitry 102, memory 104, power supply circuitry 106, inputcomponent circuitry 108, output component circuitry 110, sensorcircuitry 112, and communications circuitry 114. Electronic device 100may also include a bus 115 that may provide one or more wired orwireless communication links or paths for transferring data and/or powerto, from, or between various other components of device 100. In someembodiments, one or more components of electronic device 100 may becombined or omitted. Moreover, electronic device 100 may include anyother suitable components not combined or included in FIG. 1 and/orseveral instances of the components shown in FIG. 1 . For the sake ofsimplicity, only one of each of the components is shown in FIG. 1 .

Memory 104 may include one or more storage mediums, including, forexample, a hard-drive, flash memory, permanent memory such as read-onlymemory (“ROM”), semi-permanent memory such as random access memory(“RAM”), any other suitable type of storage component, or anycombination thereof. Memory 104 may include cache memory, which may beone or more different types of memory used for temporarily storing datafor electronic device applications. Memory 104 may be fixedly embeddedwithin electronic device 100 or may be incorporated onto one or moresuitable types of cards that may be repeatedly inserted into and removedfrom electronic device 100 (e.g., a subscriber identity module (“SIM”)card or secure digital (“SD”) memory card). Memory 104 may store mediadata (e.g., music and image files), software (e.g., for implementingfunctions on device 100), firmware, media information (e.g., mediacontent and/or associated metadata), preference information (e.g., mediaplayback preferences), lifestyle information (e.g., food preferences),exercise information (e.g., information obtained by exercise monitoringequipment or any suitable sensor circuitry), transaction information(e.g., information such as credit card information), wireless connectioninformation (e.g., information that may enable device 100 to establish awireless connection), subscription information (e.g., information thatkeeps track of podcasts or television shows or other media a usersubscribes to), contact information (e.g., telephone numbers and e-mailaddresses), calendar information, pass information (e.g., transportationboarding passes, event tickets, coupons, store cards, financial paymentcards, etc.), any other suitable data, or any combination thereof.

Power supply circuitry 106 can include any suitable circuitry forreceiving and/or generating power, and for providing such power to oneor more of the other components of electronic device 100. For example,power supply circuitry 106 can be coupled to a power grid (e.g., whendevice 100 is not acting as a portable device or when a battery of thedevice is being charged at an electrical outlet with power generated byan electrical power plant). As another example, power supply circuitry106 can be configured to generate power from a natural source (e.g.,solar power using solar cells). As another example, power supplycircuitry 106 can include one or more batteries for providing power(e.g., when device 100 is acting as a portable device). For example,power supply circuitry 106 can include one or more of a battery (e.g., agel, nickel metal hydride, nickel cadmium, nickel hydrogen, lead acid,or lithium-ion battery), an uninterruptible or continuous power supply(“UPS” or “CPS”), and circuitry for processing power received from apower generation source (e.g., power generated by an electrical powerplant and delivered to the user via an electrical socket or otherwise).The power can be provided by power supply circuitry 106 as alternatingcurrent or direct current, and may be processed to transform power orlimit received power to particular characteristics. For example, thepower can be transformed to or from direct current, and constrained toone or more values of average power, effective power, peak power, energyper pulse, voltage, current (e.g., measured in amperes), or any othercharacteristic of received power. Power supply circuitry 106 can beoperative to request or provide particular amounts of power at differenttimes, for example, based on the needs or requirements of electronicdevice 100 or periphery devices that may be coupled to electronic device100 (e.g., to request more power when charging a battery than when thebattery is already charged).

One or more input components 108 may be provided to permit a user tointeract or interface with device 100. For example, input componentcircuitry 108 can take a variety of forms, including, but not limitedto, a touch pad, dial, click wheel, scroll wheel, touch screen, one ormore buttons (e.g., a keyboard), mouse, joy stick, track ball,microphone, still image camera, video camera, scanner (e.g., a bar codescanner or any other suitable scanner that may obtain productidentifying information from a code, such as a bar code, or the like),proximity sensor, light detector, biometric sensor (e.g., a fingerprintreader or other feature recognition sensor, which may operate inconjunction with a feature-processing application that may be accessibleto electronic device 100 for authenticating a user), line-in connectorfor data and/or power, and combinations thereof. Each input component108 can be configured to provide one or more dedicated control functionsfor making selections or issuing commands associated with operatingdevice 100.

Electronic device 100 may also include one or more output components 110that may present information (e.g., graphical, audible, and/or tactileinformation) to a user of device 100. For example, output componentcircuitry 110 of electronic device 100 may take various forms,including, but not limited to, audio speakers, headphones, line-outconnectors for data and/or power, visual displays, infrared ports,tactile/haptic outputs (e.g., rumblers, vibrators, etc.), andcombinations thereof. As a particular example, electronic device 100 mayinclude a display output component as output component 110, where such adisplay output component may include any suitable type of display orinterface for presenting visual data to a user. A display outputcomponent may include a display embedded in device 100 or coupled todevice 100 (e.g., a removable display). A display output component mayinclude, for example, a liquid crystal display (“LCD”), a light emittingdiode (“LED”) display, an organic light-emitting diode (“OLED”) display,a surface-conduction electron-emitter display (“SED”), a carbon nanotubedisplay, a nanocrystal display, any other suitable type of display, orcombination thereof. Alternatively, a display output component caninclude a movable display or a projecting system for providing a displayof content on a surface remote from electronic device 100, such as, forexample, a video projector, a head-up display, or a three-dimensional(e.g., holographic) display. As another example, a display outputcomponent may include a digital or mechanical viewfinder, such as aviewfinder of the type found in compact digital cameras, reflex cameras,or any other suitable still or video camera. A display output componentmay include display driver circuitry, circuitry for driving displaydrivers, or both, and such a display output component can be operativeto display content (e.g., media playback information, applicationscreens for applications implemented on electronic device 100,information regarding ongoing communications operations, informationregarding incoming communications requests, device operation screens,etc.) that may be under the direction of processor 102.

It should be noted that one or more input components and one or moreoutput components may sometimes be referred to collectively herein as aninput/output (“I/O”) component or I/O circuitry or I/O interface (e.g.,input component 108 and output component 110 as I/O component or I/Ointerface 109). For example, input component 108 and output component110 may sometimes be a single I/O component 109, such as a touch screen,that may receive input information through a user's touch (e.g.,multi-touch) of a display screen and that may also provide visualinformation to a user via that same display screen.

Sensor circuitry 112 may include any suitable sensor or any suitablecombination of sensors operative to detect movements of electronicdevice 100 and/or any other characteristics of device 100 or itsenvironment (e.g., physical activity or other characteristics of a userof device 100). For example, sensor circuitry 112 may include one ormore three-axis acceleration motion sensors (e.g., an accelerometer)that may be operative to detect linear acceleration in three directions(i.e., the x- or left/right direction, the y- or up/down direction, andthe z- or forward/backward direction). As another example, sensorcircuitry 112 may include one or more single-axis or two-axisacceleration motion sensors that may be operative to detect linearacceleration only along each of the x- or left/right direction and they- or up/down direction, or along any other pair of directions. In someembodiments, sensor circuitry 112 may include an electrostaticcapacitance (e.g., capacitance-coupling) accelerometer that may be basedon silicon micro-machined micro electro-mechanical systems (“MEMS”)technology, including a heat-based MEMS type accelerometer, apiezoelectric type accelerometer, a piezo-resistance type accelerometer,and/or any other suitable accelerometer (e.g., which may provide apedometer or other suitable function). In some embodiments, sensorcircuitry 112 may be operative to directly or indirectly detectrotation, rotational movement, angular displacement, tilt, position,orientation, motion along a non-linear (e.g., arcuate) path, or anyother non-linear motions. Additionally or alternatively, sensorcircuitry 112 may include one or more angular rate, inertial, and/orgyro-motion sensors or gyroscopes for detecting rotational movement. Forexample, sensor circuitry 112 may include one or more rotating orvibrating elements, optical gyroscopes, vibrating gyroscopes, gas rategyroscopes, ring gyroscopes, magnetometers (e.g., scalar or vectormagnetometers), compasses, and/or the like. Any other suitable sensorsmay also or alternatively be provided by sensor circuitry 112 fordetecting motion on device 100, such as any suitable pressure sensors,altimeters, or the like. Using sensor circuitry 112, electronic device100 may be configured to determine a velocity, acceleration,orientation, and/or any other suitable motion attribute of electronicdevice 100.

Sensor circuitry 112 may include any suitable sensor(s), including, butnot limited to, one or more of a GPS sensor, accelerometer, directionalsensor (e.g., compass), gyroscope, motion sensor, pedometer, passiveinfrared sensor, ultrasonic sensor, microwave sensor, a tomographicmotion detector, a camera, a biometric sensor, a light sensor, a timer,or the like. In some examples, a biometric sensor may include, but isnot limited to, one or more health-related optical sensors, capacitivesensors, thermal sensors, electric field (“eField”) sensors, and/orultrasound sensors, such as photoplethysmogram (“PPG”) sensors,electrocardiography (“ECG”) sensors, galvanic skin response (“GSR”)sensors, posture sensors, stress sensors, photoplethysmogram sensors,and/or the like. These sensors can generate data providinghealth-related information associated with the user. For example, PPGsensors can provide information regarding a user's respiratory rate,blood pressure, and/or oxygen saturation. ECG sensors can provideinformation regarding a user's heartbeats. GSR sensors can provideinformation regarding a user's skin moisture, which may be indicative ofsweating and can prioritize a thermostat application to determine auser's body temperature. In some examples, each sensor can be a separatedevice, while, in other examples, any combination of two or more of thesensors can be included within a single device. For example, agyroscope, accelerometer, photoplethysmogram, galvanic skin responsesensor, and temperature sensor can be included within a wearableelectronic device, such as a smart watch, while a scale, blood pressurecuff, blood glucose monitor, SpO2 sensor, respiration sensor, posturesensor, stress sensor, and asthma inhaler can each be separate devices.While specific examples are provided, it should be appreciated thatother sensors can be used and other combinations of sensors can becombined into a single device. Using one or more of these sensors,device 100 can determine physiological characteristics of the user whileperforming a detected activity, such as a heart rate of a userassociated with the detected activity, average body temperature of auser detected during the detected activity, any normal or abnormalphysical conditions associated with the detected activity, or the like.In some examples, a GPS sensor or any other suitable location detectioncomponent(s) of device 100 can be used to determine a user's locationand movement, as well as a displacement of the user's motion. Anaccelerometer, directional sensor, and/or gyroscope can further generateactivity data that can be used to determine whether a user of device 100is engaging in an activity, is inactive, or is performing a gesture.Device 100 can further include a timer that can be used, for example, toadd time dimensions to various attributes of the detected physicalactivity, such as a duration of a user's physical activity orinactivity, time(s) of a day when the activity is detected or notdetected, and/or the like. One or more sensors of sensor circuitry orcomponent 112 may be embedded in a body (e.g., housing 101) of device100, such as a long a bottom surface that may be operative to contact auser, or can be positioned at any other desirable location. In someexamples, different sensors can be placed in different locations insideor on the surfaces of device 100 (e.g., some located inside housing 101)and some attached to an attachment mechanism (e.g., a wrist band coupledto a housing of a wearable device), or the like. In other examples, oneor more sensors can be worn by a user separately from device 100. Insuch cases, the sensors can be configured to communicate with device 100using a wired and/or wireless technology (e.g., via communicationscircuitry 114). In some examples, sensors can be configured tocommunicate with each other and/or share data collected from one or moresensors. In some other examples, device 100 can be waterproof such thatthe sensors can detect a user's activity in water.

Communications circuitry 114 may be provided to allow device 100 tocommunicate with one or more other electronic devices or servers usingany suitable communications protocol. For example, communicationscircuitry 114 may support Wi-Fi™ (e.g., an 802.11 protocol), ZigBee™(e.g., an 802.15.4 protocol), WiDi™, Ethernet, Bluetooth™, Bluetooth™Low Energy (“BLE”), high frequency systems (e.g., 900 MHz, 2.4 GHz, and5.6 GHz communication systems), infrared, transmission controlprotocol/internet protocol (“TCP/IP”) (e.g., any of the protocols usedin each of the TCP/IP layers), Stream Control Transmission Protocol(“SCTP”), Dynamic Host Configuration Protocol (“DHCP”), hypertexttransfer protocol (“HTTP”), BitTorrent™, file transfer protocol (“FTP”),real-time transport protocol (“RTP”), real-time streaming protocol(“RTSP”), real-time control protocol (“RTCP”), Remote Audio OutputProtocol (“RAOP”), Real Data Transport Protocol™ (“RDTP”), User DatagramProtocol (“UDP”), secure shell protocol (“SSH”), wireless distributionsystem (“WDS”) bridging, any comniunications protocol that may be usedby wireless and cellular telephones and personal e-mail devices (e.g.,Global System for Mobile Communications (“GSM”), GSM plus Enhanced Datarates for GSM Evolution (“EDGE”), Code Division Multiple Access(“CDMA”), Orthogonal Frequency-Division Multiple Access (“OFDMA”), highspeed packet access (“HSPA”), multi-band, etc.), any communicationsprotocol that may be used by a low power Wireless Personal Area Network(“6LoWPAN”) module, Near Field Communication (“NFC”), any othercommunications protocol, or any combination thereof. Communicationscircuitry 114 may also include or be electrically coupled to anysuitable transceiver circuitry that can enable device 100 to becommunicatively coupled to another device (e.g., a host computer or anaccessory device) and communicate with that other device wirelessly, orvia a wired connection (e.g., using a connector port). Communicationscircuitry 114 may be configured to determine a geographical position ofelectronic device 100. For example, communications circuitry 114 mayutilize the global positioning system (“GPS”) or a regional or site-widepositioning system that may use cell tower positioning technology orWi-Fi™ technology.

Processing circuitry 102 of electronic device 100 may include anyprocessing circuitry that may be operative to control the operations andperformance of one or more components of electronic device 100. Forexample, processor 102 may receive input signals from any inputcomponent 108 and/or sensor circuitry 112 and/or communicationscircuitry 114 and/or drive output signals through any output component110 and/or communications circuitry 114. As shown in FIG. 1 , processor102 may be used to run at least one application 103. Application 103 mayinclude, but is not limited to, one or more operating systemapplications, firmware applications, software applications, algorithmicmodules, media analysis applications, media playback applications, mediaediting applications, communications applications, pass applications,calendar applications, social media applications, state determinationapplications, biometric feature-processing applications, activitymonitoring applications, activity motivating applications, and/or anyother suitable applications. For example, processor 102 may loadapplication 103 as a user interface program to determine howinstructions or data received via an input component 108 and/or anyother component of device 100 may manipulate the one or more ways inwhich information may be stored and/or provided to the user via anoutput component 110 and/or any other component of device 100. Anyapplication 103 may be accessed by any processing circuitry 102 from anysuitable source, such as from memory 104 (e.g., via bus 115) and/or fromanother device or server (e.g., remote server 50) (e.g., viacommunications circuitry 114). Processor 102 may include a singleprocessor or multiple processors. For example, processor 102 may includeat least one “general purpose” microprocessor, a combination of generaland special purpose microprocessors, instruction set processors,graphics processors, video processors, communications processors, motionprocessors, biometric processors, application processors, and/or relatedchips sets, and/or special purpose microprocessors. Processor 102 alsomay include on board memory for caching purposes.

Processor 102 may be configured to capture (e.g., with an inputcomponent 108) or otherwise access (e.g., from memory 104 and/orcommunications circuitry 114) and process any suitable library 105 ofany suitable amount of media content pieces (e.g., any media contentand/or associated metadata) for managing the media content pieces in aneffective and user-friendly manner. Media content pieces (“MCPs”) mayinclude any suitable type of asset or item of media content, such asimage content (e.g., pixel values for one or more photographs or videoframes) and/or audio content (e.g., one or more audio tracks that may ormay not be associated with video frames as audio/visual video content)and/or text content (e.g., an E-book, etc.) and/or haptic content (e.g.,vibrations or motions that may be provided in connection with othermedia, such as a video), where examples of different types of visualmedia content of an MCP may include, but are not limited to, a stillphoto, a video clip, a burst-mode photo sequence, a panoramic photo, atime lapse video, a slow motion video, a short video that may becaptured alongside a photograph (e.g., a Live Photo™ available by AppleInc.), and/or the like. An MCP may also include any suitable amount ofany suitable type(s) of metadata assets or metadata content (metadata)that may describe one or more characteristics of and be associated withthe media content (e.g., an image, a video, etc.) of the MCP, including,but not limited to, captured metadata, post-capture metadata, derivedmetadata, explicit user-assigned metadata, and/or the like.Additionally, processor 102 may be configured to generate or otherwiseaccess (e.g., from memory 104 and/or communications circuitry 114) anMCP management system 107 (e.g., a database (e.g., a relational database(e.g., a tabular database, etc.), a distributed database that can bedispersed or replicated among different points in a network, anobject-oriented programming database that can be congruent with the datadefined in object classes and subclasses, etc.) and/or a knowledge graphmetadata network) that may be operative to be used by processor 102(e.g., along with or as a portion of any suitable application 103) tomanage, store, ingest, organize, and/or retrieve the various MCPs oflibrary 105. In some examples where device 100 may collect and/orprocess a relatively large MCP library 105 and/or use relatively largeMCP management systems 107, device 100 may not have enough memorycapacity to collect and process and store all of the data for such alibrary and/or management system and can instead be configured tooffload some or all of the data on an external device that may be remotefrom device 100 (e.g., server 50, which, although not shown, may beconfigured to include, one, some, each, and/or multiple ones of thecomponents of device 100). The external device can be configured tocommunicate with a plurality of devices 100, and store data collectedfrom these devices. The external device can be further configured toexecute computer instructions on the data and communicate the resultwith one or more of these devices 100.

Electronic device 100 may also be provided with a housing 101 that mayat least partially enclose one or more of the components of device 100for protection from debris and other degrading forces external to device100. In some embodiments, one or more of the components may be providedwithin its own housing (e.g., input component 108 may be an independentkeyboard or mouse within its own housing that may wirelessly or througha wire communicate with processor 102, which may be provided within itsown housing).

As shown in FIG. 2 , one specific example of electronic device 100 maybe an electronic device, such as an iPhone™, where housing 101 may allowaccess to various input components 108 a-108 i, various outputcomponents 110 a-110 c, and various I/O components 109 a-109 c throughwhich device 100 and a user and/or an ambient environment may interfacewith each other. Input component 108 a may include a button that, whenpressed, may cause a “home” screen or menu of a currently runningapplication to be displayed by device 100. Input component 108 b may bea button for toggling electronic device 100 between a sleep mode and awake mode or between any other suitable modes. Input component 108 c mayinclude a two-position slider that may disable one or more outputcomponents 112 in certain modes of electronic device 100. Inputcomponents 108 d and 108 e may include buttons for increasing anddecreasing the volume output or any other characteristic output of anoutput component 110 of electronic device 100. Each one of inputcomponents 108 a-108 e may be a mechanical input component, such as abutton supported by a dome switch, a sliding switch, a control pad, akey, a knob, a scroll wheel, or any other suitable form.

An output component 110 a may be a display that can be used to display avisual or graphic user interface (“GUI”) 180, which may allow a user tointeract with electronic device 100. GUI 180 may include various layers,windows, screens, templates, elements, menus, and/or other components ofa currently running application (e.g., application 103) that may bedisplayed in all or some of the areas of display output component 110 a.One or more of user input components 108 a-108 i may be used to navigatethrough GUI 180. For example, one user input component 108 may include ascroll wheel that may allow a user to select one or more graphicalelements or icons 182 of GUI 180. Icons 182 may also be selected via atouch screen I/O component 109 a that may include display outputcomponent 110 a and an associated touch input component 108 f. Such atouch screen I/O component 109 a may employ any suitable type of touchscreen input technology, such as, but not limited to, resistive,capacitive, infrared, surface acoustic wave, electromagnetic, or nearfield imaging. Furthermore, touch screen I/O component 109 a may employsingle point or multi-point (e.g., multi-touch) input sensing.

Icons 182 may represent various applications, layers, windows, screens,templates, elements, and/or other components that may be displayed insome or all of the areas of display component 110 a upon selection bythe user. Furthermore, selection of a specific icon 182 may lead to ahierarchical navigation process. For example, selection of a specificicon 182 may lead from screen 190 of FIG. 2 to a new screen of GUI 180that may include one or more additional icons or other GUI elements ofthe same application or of a new application associated with that icon182. Textual indicators 181 may be displayed on or near each icon 182 tofacilitate user interpretation of each graphical element icon 182. It isto be appreciated that GUI 180 may include various components arrangedin hierarchical and/or non-hierarchical structures. When a specific icon182 is selected, device 100 may be configured to open a new applicationassociated with that icon 182 and display a corresponding screen of GUI180 associated with that application. For example, when the specificicon labeled with a “Photos” textual indicator is selected, device 100may launch or otherwise access a media management and editingapplication (e.g., Photos™ available by Apple Inc.) that may provideuser access to one or more collections of MCPs (e.g., photos and/orvideos) and may display screens of a specific user interface that mayinclude one or more tools or features for interacting with mediacontent. As another example, when the specific icon labeled with a“Calendar” textual indicator is selected, device 100 may launch orotherwise access a specific calendar or reminder application and maydisplay screens of a specific user interface that may include one ormore tools or features for interacting with one or more events or otherreminders that may be time-sensitive in a specific manner. As anotherexample, when the specific icon labeled with a “Wallet” textualindicator is selected, device 100 may launch or otherwise access aspecific pass or wallet application and may display screens of aspecific user interface that may include one or more tools or featuresfor interacting with one or more passes or other credentials (e.g.,payment credentials of an NFC component) in a specific manner. Asanother example, when the specific icon labeled with a “Contacts”textual indicator is selected, device 100 may launch or otherwise accessa specific contacts or address book application and may display screensof a specific user interface that may include one or more tools orfeatures for interacting with one or more contacts of one or morepersons or businesses or other entities in a specific manner. As anotherexample, when the specific icon labeled with a “Social Media” textualindicator is selected, device 100 may launch or otherwise access aspecific social media application or site and may display screens of aspecific user interface that may include one or more tools or featuresfor interacting with one or more social media networks with which a usermay or may not have an account in a specific manner. As another example,when the specific icon labeled with a “Weather” textual indicator isselected, device 100 may launch or otherwise access a specific weatherapplication or site and may display screens of a specific user interfacethat may include one or more tools or features for determining orpresenting the current and/or past and/or future weather and/or otherenvironmental conditions local to and/or distant from device 100 in aspecific manner (e.g., as may be detected by any suitable sensors ofdevice 100 and/or of remote server 50). As another example, when thespecific icon labeled with a “Health” textual indicator is selected,device 100 may launch or otherwise access a specific health applicationor site and may display screens of a specific user interface that mayinclude one or more tools or features for determining or presenting thecurrent and/or past health activities and/or biometric characteristicsof a user (e.g., as may be detected by any suitable sensors of device100 and/or of remote server 50) in a specific manner. For eachapplication, screens may be displayed on display output component 110 aand may include various user interface elements. Additionally oralternatively, for each application, various other types of non-visualinformation may be provided to a user via various other outputcomponents 110 of device 100.

Electronic device 100 also may include various other I/O components 109that may allow for communication between device 100 and other devices,such as a connection port 109 b that may be configured for transmittingand receiving data files, such as media files or customer order files,and/or any suitable information (e.g., audio signals) from a remote datasource and/or power from an external power source. For example, I/Ocomponent 109 b may be any suitable port (e.g., a Lightning™ connectoror a 30-pin dock connector available by Apple Inc.). I/O component 109 cmay be a connection slot for receiving a SIM card or any other type ofremovable component. Electronic device 100 may also include at least oneaudio input component 110 g, such as a microphone, and at least oneaudio output component 110 b, such as an audio speaker. Electronicdevice 100 may also include at least one tactile output component 110 c(e.g., a rumbler, vibrator, haptic and/or taptic component, etc.), acamera and/or scanner input component 108 h (e.g., a video or stillcamera, and/or a bar code scanner or any other suitable scanner that mayobtain product identifying information from a code, such as a bar code,or the like), and a biometric input component 108 i (e.g., a fingerprintreader or other feature recognition sensor, which may operate inconjunction with a feature-processing application that may be accessibleto electronic device 100 for authenticating a user).

FIG. 3 is an illustrative schematic of MCP library 105, which mayinclude any suitable number of MCPs 305 of any suitable type. Library105 may at least partially reside on device 100 (e.g., in memory 104)and/or may at least partially reside on a remote server (e.g., server50) that may be accessible to device 100. As shown in FIG. 3 , forexample, at least one, some, or each MCP 305 may include an asset oritem of MCP media content or an MCP media content item (“MCI”) 310(e.g., an image, a video, etc.) and associated MCP metadata content 311.MCP metadata content 311 may include any suitable number of metadataassets of any suitable type(s) of metadata, including, but not limitedto, captured metadata 315 and post-capture metadata 320. Capturedmetadata 315 may include any suitable metadata that may be generated byor associated with characteristics of the capture device that capturedthe associated media content 310 (e.g., by camera input component 108 hand/or any other suitable component(s) of device 100 or by any suitablecomponent(s) of any other suitable media capture device (e.g., server50)) at the time that such media content is captured. Examples ofcapture metadata 315 may include, but are not limited to, date and timeof media content capture (e.g., based on a clock of device 100),location of media content capture (e.g., based on GPS or any otherlocation service of device 100), one or media capture device settings ofmedia content capture (e.g., any suitable settings of camera inputcomponent 108 h, such as exposure, flash, white point, etc.), and/or thelike.

Post-capture metadata 320 may include any suitable type(s) of metadatathat may be defined for the media content after the media content hasbeen capture. As shown, for example, two exemplary types of post-capturemetadata 320 may include derived metadata 325 and explicit user-assignedmetadata 330. Explicit user-assigned metadata 330 may include anysuitable keywords (e.g., birthday, vacation, anniversary, etc.) or othersuitable tags (e.g., like, dislike, favorite, verification of identityof one or more content indicators (e.g., faces or locations or scenes orclusters of features or indicators or otherwise) in the media content,etc.) that a user may assign to or otherwise associate with the mediacontent and/or one or more user-specified associations for the MCP withrespect to other MCPs (e.g., inclusion of the MCP in a user-specifiedalbum (e.g., photo album) or other collection type of MCPs). Suchuser-assignment of any suitable user-assigned metadata 330 may beaccomplished via any suitable user interface application that may bepresented by device 100 and/or server 50 to a user of system 1.

Derived metadata 325 of an MCP 305 may include any suitable types ofmetadata assets that may be derived or inferred by processor analysis(e.g., by an application 103 of processor 102) of media content 310,captured metadata 315, user-assigned metadata 330, and/or any useractions that may be associated with that MCP 305. One or more frameworkservices (e.g., service(s) of device 100 and/or of server 50) mayanalyze one or more MCPs 305, their media content, their metadata,and/or any associated user actions to produce derived metadata 325 forone or more of the MCPs. Examples of such derived metadata 325 mayinclude, but are not limited to, derived domain metadata 332, mediacontent-analysis metadata 335 (e.g., image-analysis metadata for animage MCI 310), and/or implicit user metadata 340. Implicit usermetadata 340 may be any suitable metadata that may be generated bymonitoring any user actions with respect to the MCP (e.g., sharing theMCP with others, repeatedly viewing the MCP, etc.).

Media content-analysis metadata 335 may include any suitable type(s) ofmetadata attributes that may be determined by analyzing MCP mediacontent 310. In some embodiments, such media content-analysis metadata335 may include any suitable media attribute score metadata 345 and/orany suitable media content indicator metadata 350. Examples of mediaattribute score metadata 345 may include, but are not limited to, anysuitable media attribute scores for quantifying focus, exposure, blur,sharpness, color attributes, pixel characteristics, pixel intensityvalues, luminance values, brightness values, and/or the like for anyimage MCI and/or for any frame(s) of any video MCI, and/or forquantifying volume, pitch, timbre, voice, source (e.g., detected soundis human voice, detected sound is fire truck, etc.) and/or the like forany audio MCI and/or the like for any other type of MCI. Media attributescore metadata 345 may be generated and/or otherwise obtained by one ormore suitable services (e.g., framework services) of system 1 (e.g., ofdevice 100 and/or of server 50). Examples of media content indicatormetadata 350 may include, but are not limited to, any suitable mediacontent indicators that may be indicative of the type of its associatedMCI 310 and/or that may characterize its associated MCI 310 in anysuitable manner. In an example when an MCI 310 is an image, one or moremedia content indicators of associated media content indicator metadata350 may reference one or more types of particular content indicatorsthat may be valid for that image. Additionally or alternatively, when anMCI 310 may be a video clip with multiple frames or images, one or moremedia content indicators of associated media content indicator metadata350 may be expressed in terms of ranges that may define the range ofimages or frames over which a particular content indicator may be valid.Examples of particular types of content indicators of media contentindicator metadata 350 may include, but are not limited to, faceindicators (e.g., unique face vectors (or any other suitable statisticalrepresentations)), smile indicators, voice indicators, camera motionindicators, junk content indicators, scene indicators, image qualityindicators, and/or the like. Any such content indicators 350 may begenerated or obtained (e.g., as one or more feature vectors or featureindicators) by one or more suitable services (e.g., framework services)of system 1 (e.g., of device 100 and/or of server 50).

Derived domain metadata 332 may include any suitable data associatedwith any suitable domain type(s) that may be associated with the MCP byanalyzing the metadata already associated with the MCP. For example, insome embodiments, a domain type may be a location domain and anycaptured location metadata of captured metadata 315, explicit usermetadata 330, media content-analysis metadata 335 (e.g., media attributescore(s) 345 and/or content indicator(s) 350), and/or implicit usermetadata 340 and/or other derived metadata 325 for any MCP or collectionof MCPs may be analyzed with or without any contextual data in order toassociate an MCP with any suitable derived location metadata 332 thatmay be indicative of one or more location regions and/or one or morelocation areas (e.g., areas of interest) and/or one or more locationdesignations (e.g., home, residence, office, etc.) and/or the like thatmay enable the MCP to be grouped with other MCPs. As another example, adomain type may be a time domain and any captured time metadata ofcaptured metadata 315, explicit user metadata 330, mediacontent-analysis metadata 335 (e.g., media attribute score(s) 345 and/orcontent indicator(s) 350), and/or implicit user metadata 340 and/orother derived metadata 325 for any MCP or collection of MCPs may beanalyzed with or without any contextual data in order to associate anMCP with any suitable derived time metadata 332 that may be indicativeof one or more time quantifications (e.g., weekday, season, etc.) and/orone or more time event designations (e.g., holiday, Halloween, etc.)and/or the like that may enable the MCP to be grouped with other MCPs.As another example, a domain type may be a person domain and anycaptured person metadata of captured metadata 315, explicit usermetadata 330, media content-analysis metadata 335 (e.g., media attributescore(s) 345 and/or content indicator(s) 350), and/or implicit usermetadata 340 and/or other derived metadata 325 for any MCP or collectionof MCPs may be analyzed with or without any contextual data in order toassociate an MCP with any suitable derived person metadata 332 that maybe indicative of one or more person quantifications (e.g., person namesand/or person relationships, such as John Doe (“user”), Jane Doe(“user's wife”), Jenn Doe (“user's daughter”), unverified person(unknown relationship), Jim James (“user's co-worker”), etc.) and/or oneor more person event designations (e.g., anniversary, birthday, etc.)and/or one or more person social group designations (e.g., co-workersocial group of John Doe and Jim James, any social group collection ofidentities that may be identified to appear together often (e.g., indifferent moments, at different events, etc.), etc.) and/or the likethat may enable the MCP to be grouped with other MCPs. As anotherexample, a domain type may be a place domain and any captured placemetadata of captured metadata 315, explicit user metadata 330, mediacontent-analysis metadata 335 (e.g., media attribute score(s) 345 and/orcontent indicator(s) 350), and/or implicit user metadata 340 and/orother derived metadata 325 for any MCP or collection of MCPs may beanalyzed with or without any contextual data in order to associate anMCP with any suitable derived place metadata 332 that may be indicativeof one or more points of interest (“POIs”) and/or regions of interest(“ROIs”) (e.g., nature, water, mountain, urban, beach, nightlife,restaurant, entertainment, park, culture, travel, shopping, etc.) and/orthe like that may enable the MCP to be grouped with other MCPs. Asanother example, a domain type may be a scene domain and any capturedscene metadata of captured metadata 315, explicit user metadata 330,media content-analysis metadata 335 (e.g., media attribute score(s) 345and/or content indicator(s) 350), and/or implicit user metadata 340and/or other derived metadata 325 for any MCP or collection of MCPs maybe analyzed with or without any contextual data in order to associate anMCP with any suitable derived scene metadata 332 that may be indicativeof one or more scenes (e.g., animal (e.g., bird, reptile, dog, fish,etc.), outdoor (e.g., sky, sand, playground, etc.), celebration (e.g.,wedding, birthday cake, jack-o-lantern, etc.), structure (e.g.,fireplace, aquarium, etc.), vehicle (e.g., helicopter, bicycle,limousine, etc.), recreation (e.g., performance (e.g., orchestra,karaoke, rodeo, etc.), sport (e.g., rafting, surfing, scuba, etc.),etc.), plant (e.g., flower, tree, etc.), game (e.g., poker, foosball,etc.), fire, liquid (e.g., jacuzzi, river, etc.), art (e.g., origami,balloon, etc.), light (e.g., chandelier, candle, etc.), room (e.g., bar,museum, restaurant, etc.), people, etc.) and/or the like that may enablethe MCP to be grouped with other MCPs. As another example, a domain typemay be a moment domain and any captured moment metadata of capturedmetadata 315, explicit user metadata 330, media content-analysismetadata 335 (e.g., media attribute score(s) 345 and/or contentindicator(s) 350), and/or implicit user metadata 340 and/or otherderived metadata 325 for any MCP or collection of MCPs may be analyzedwith or without any contextual data in order to associate an MCP withany suitable derived moment metadata 332 that may be indicative of amoment (e.g., a distinct range of times and a distinct location ordistinct range of locations) and/or the like that may enable the MCP tobe grouped with other MCPs. As described in more detail with respect toFIG. 4 , for example, one or more applications or services (e.g., aframework services) of system 1 (e.g., of processor 102 and/or server50) may be operative to generate and/or use an MCP management system 107(e.g., a database and/or a knowledge graph metadata network (e.g., ahierarchical directed acyclic graph (“DAG”) structure that may includenodes corresponding to different domain types and different specificsub-domains of each domain type of metadata 311, for example, wherederived domain metadata 332 may be defined in terms of node identifiersin the graph structure, and all nodes of the structure may be correlated(e.g., by correlation weights (e.g., confidence weights and/or relevanceweights))) to manage, store, ingest, organize, and/or retrieve thevarious MCPs 305 (e.g., metadata 311 and/or content 310) of library 105.Additional disclosure regarding suitable graph metadata networks can befound in co-pending, commonly-assigned U.S. Patent ApplicationPublication No. 2017/0091154 (published on Mar. 30, 2017) and inco-pending, commonly-assigned U.S. patent application Ser. No.15/391,269 (filed on Dec. 27, 2016), each of which is herebyincorporated by reference herein in its entirety.

Therefore, there may be various types of metadata assets 311 that may beassociated with an MCI 310 of an MCP 305. A particular type of metadataasset may be a first metadata asset 311 of a first MCP 305 associatedwith a first MCI 310 and may also be a second metadata asset 311 of asecond MCP 305 associated with a second MCI 310. In some embodiments, atype of metadata may be categorized as primitive metadata or inferredmetadata, which may be determined based at least on primitive metadata.For example, as may be used herein, “primary primitive metadata” mayrefer to metadata that may describe one or more characteristics orattributes associated with one or more MCIs 310. Some types of primaryprimitive metadata include, but are not limited to, one or more of timemetadata, geo-position metadata, geolocation metadata, people metadata,scene metadata, content metadata, object metadata, and/or soundmetadata. Time metadata may refer to a time that may be associated withone or more media content items (e.g., a timestamp associated with amedia content item, a time at which the media content item was capturedor otherwise generated, a time at which the media content item wasmodified, a time at which the media content item was stored, a time atwhich the media content item was transmitted, a time at which the mediacontent item was received, etc.), which may be captured metadata 315.Geo-position metadata may refer to geographic and/or spatial attributesthat may be associated with one or more media content items using anysuitable location sensing and/or geographic coordinate system (e.g.,latitude, longitude, and/or altitude, etc.), which may be capturedmetadata 315. Geolocation metadata may refer to one or more meaningfullocations rather than geographic coordinates that may be associated withone or more media content items, such as a beach (and its name), astreet address, a country name, a region, a building, a landmark, and/orthe like, which, for example, may be determined by processinggeo-position metadata together with data from a map application and/orany other suitable data available to device 100 to determine that thegeolocation for a scene in a group of images. People metadata may referto at least one face that may be detected in at least one media contentitem (e.g., through any suitable facial recognition technique(s)), wherethe people metadata may be indicative of a particular identity (e.g., atagged or otherwise known (e.g., verified) person) or an unknownidentity (e.g., an unverified or unknown person), which may be metadata330 and/or metadata 335. Scene metadata and/or object metadata may referto an overall description of an activity or situation associated withone or more media content items based on any objects that may bedetected therein (e.g., if a media content item includes a group ofimages, then scene metadata for the group of images may be determinedusing detected objects in one or more of the images (e.g., the detectionof a large cake with candles and/or balloons in at least two images inthe group can be used to associate “birthday” scene metadata with eachof the images)), where such objects or scene indicators or contentindicators may be any suitable objects (e.g., a detected animal, adetected company logo, a detected piece of furniture, a detectedinstrument, etc.) that may be able to be detected in a media contentitem using any suitable techniques (e.g., any suitable image processingtechniques), which may be metadata 350. Content metadata may refer toany features of a media content item (e.g., pixel characteristics, pixelintensity values, luminance values, brightness values, loudness levels,etc., etc.), which may be metadata 345. Sound metadata may refer to oneor more detected sounds associated with one or more media content itemsa detected sound as a human's voice, a detected sound as a fire truck'ssiren, etc.), which may be metadata 335.

As used herein, “inferred metadata” may refer to metadata that maydescribe one or more characteristics or attributes associated with oneor more MCIs 310 that is beyond the information that may be provided byprimitive metadata. One difference between primitive metadata andinferred metadata may be that primitive metadata may represent aninitial set of descriptions of one or more media content items whileinferred metadata may provide one or more additional descriptions orcharacteristics of the one or more media content items based onprocessing one or more of the primitive metadata assets (e.g., incombination with any suitable contextual data that may be available todevice 100). For example, primitive metadata may identify two detectedpersons in one or a group of images as John Doe and Jane Doe, whileinferred metadata may identify John Doe and Jane Doe as a married couplebased on processing at least a portion of the primitive metadata (e.g.,in combination with any suitable contextual data). Inferred metadata maybe determined from at least one of (i) processing of a combination ofdifferent types of primary primitive metadata, (ii) processing of acombination of different types of contextual information, and (iii)processing of a combination of primary primitive metadata and contextualinformation. As used herein, “context” and/or its variations may referto any or all data that may be accessible to device 100, such asphysical, logical, social, and/or other contextual information. As usedherein, “contextual information” and/or contextual data and/orcontextual metadata and/or its variations may refer to metadata or anyother suitable information that may describe or define a user's contextor a context of a user's device (e.g., device 100 with access to library105 (e.g., as may be associated with a user)). Exemplary contextualinformation may include, but is not limited to, a predetermined timeinterval, a time event scheduled to occur in a predetermined timeinterval, a geolocation to be visited in a predetermined time interval,one or more identified persons associated with a predetermined time, anevent scheduled for a predetermined time, a geolocation to be visited atpredetermined time, weather metadata describing weather associated witha particular period in time (e.g., rain, snow, sun, temperature, etc.),season metadata describing a season associated with capture of a mediacontent item, and/or the like. For example, such contextual informationcan be obtained from any suitable application data local to device 100and/or any suitable application data that may be provided by externalsources (e.g., a remote server (e.g., server 50 (e.g., via theinternet))) from any suitable application or data source, such as asocial networking application (e.g., information indicative ofrelationships between people, planned events with or without knownattendees, birthdays, favorite interests (e.g., hobbies, activities,etc.) and/or restaurants and/or media, etc.), a weather application(e.g., information indicative of weather or other environmentalconditions at a certain place at a certain time), a calendar application(e.g., information indicative of a scheduled event, scheduledparticipants, etc.), a contact application (e.g., information indicativeof a person's home address, etc.), a health application (e.g.,information indicative of a user's heart rate, steps taken, speed,calories burned, food ingested, particular sport, or hobby performed,etc.), a wallet application (e.g., information indicative of a scheduledor attended event, passes for an event, receipts for services and/orgoods purchased, etc.), a messaging application or an e-mail application(e.g., information indicative of discussed events, communicatingpersons, etc.), a map application (e.g., information indicative ofplaces visited, etc.), a photos application itself (e.g., informationindicative of any tags or verified face identifications, likes, shares,groupings, albums, and/or the like based on a user's interaction (e.g.,input data) with library 105), and/or any other type of application ordata source that may be operative to provide information that may beprocessed (e.g., based on and/or in combination with any known metadataof library 105) to reveal additional characteristics to be associatedwith one or more media content items (e.g., as new metadata and/orcorrelations between known metadata (e.g., to define a new node and/orcorrelation between nodes of a metadata network (e.g., a knowledgegraph)), as described in more detail with respect to FIG. 4 ).Therefore, one or metadata assets of library 105 may be indicative of aperson's name, birthplace, birthday, gender, relationship status,identification of related persons, social groups and identities ofmembers of social groups of any type (e.g., family, friends, co-workers,etc.), current and/or prior address(es) of residence and/or vacationand/or work, interests (e.g., hobbies (e.g., pets owned, instrumentsplayed, activities enjoyed, restaurants enjoyed, etc.)), places ofinterest (e.g., visited and/or interested in), etc.), religion, tripstaken and associated travelers, events attended and associatedattendees, physical activity (e.g., workouts or sports or hobbies oractivities performed or enjoyed), and/or the like. The precedingexamples are illustrative and not restrictive.

Two categories of inferred metadata may be referred to herein as primaryinferred metadata and auxiliary inferred metadata. Primary inferredmetadata may include time event metadata that may describe one or moretime events associated with one or more media content items. Forexample, if a media content item or a collection of media content itemsis associated with primary primitive metadata indicative of a particulartime or a particular range of times and/or a particular location, thenassociated primary inferred metadata may be determined to include timeevent metadata that may describe one or more time events or peopleevents associated with such time and/or location information (e.g., avacation, a birthday, a sporting event, a concert, a graduationceremony, a dinner, a project, a work-out session, a traditionalholiday, etc.), where such primary inferred metadata may, in someembodiments, be determined by analyzing such primary primitive metadataalone or in combination with any suitable contextual metadata (e.g.,calendar data and/or social media data, etc.). Auxiliary inferredmetadata may be any suitable metadata including, but not limited to,geolocation relationship metadata, person relationship metadata, objectrelationship metadata, and sound relationship metadata. Geolocationrelationship metadata may refer to a relationship between one or moreknown persons associated with one or more media content items and one ormore locations associated with the one or more media content items. Forexample, an analytics engine or data mining technique can be used todetermine that a scene associated with one or more media content itemsof John Doe represents John Doe's home. Person relationship metadata mayrefer to a relationship between one or more known persons associatedwith one or more media content items and one or more other known personsassociated with the one or more media content items. For example, ananalytics engine or data mining technique can be used to determine thatJane Doe (who appears in one or more images with John Doe) is John Doe'swife. Object relationship metadata may refer to a relationship betweenone or more known persons associated with one or more media contentitems and one or more known objects associated with the one or moremedia content items. For example, an analytics engine or data miningtechnique can be used to determine that a boat appearing in one or moreimages with John Doe is owned by John Doe. Sound relationship metadatamay refer to a relationship between one or more known sounds associatedwith one or more media content items and one or more known personsassociated with the one or more media content items. For example, ananalytics engine or data mining technique can be used to determine thata voice that appears in one or more videos with John Doe is John Doe'svoice.

Inferred metadata may be determined or inferred from primitive metadataand/or contextual information by performing any suitable type(s) ofprocessing, including, but not limited to, data mining primitivemetadata and/or contextual information; analyzing primitive metadataand/or contextual information, applying logical rules to primitivemetadata and/or to contextual information, and/or any other knownmethods that may be used to infer new information from provided oracquired information. In some embodiments, primitive metadata can beextracted from inferred metadata. For example, primary primitivemetadata (e.g., time metadata, geolocation metadata, scene metadata,etc.) can be extracted from primary inferred metadata (e.g., time eventmetadata, etc.). Techniques for determining inferred metadata and/orextracting primitive metadata from inferred metadata can be iterative.For example, inferring metadata can trigger the inference of othermetadata and so on. As another example, extracting primitive metadatafrom inferred metadata can trigger inference of additional inferredmetadata or extraction of additional primitive metadata.

FIG. 4 shows, in block diagram form, an exemplary portion 107 n of atype of MCP management system 107, which may be provided by any suitablegraph structure, such as a DAG structure or otherwise, and may also bereferred to herein as a knowledge graph metadata network or knowledgegraph or metadata network 107 n. While an MCP management system may,alternatively, be provided by any suitable database (e.g., a relationaldatabase, a distributed database, an object-oriented programmingdatabase, etc.), using such a database for management of a library ofMCPs may be too resource-intensive (e.g., substantial computationalresources may be needed to manage the MCPs (e.g., substantial processingpower may be needed for performing queries or transactions, storagememory space for storing the necessary databases, etc.)) and/or may notbe as easily implemented on a computing system with limited storagecapacity (e.g., device 100), thereby requiring certain functionality ofa remote subsystem (e.g., remote server 50). Instead, in someembodiments, MCP management system 107 may be at least partiallyprovided as a metadata network 107 n, at least an exemplary portion ofwhich may be shown in FIG. 4 , that may include correlated metadataassets that may describe characteristics associated with various MCIs310 of MCPs 305 of library 105, where such a metadata network may beoperative to manage library 105 locally on device 100 (e.g., withprocessing circuitry 102) without the need for any external data sources(e.g., remote server 50). Each metadata asset may be a type of metadata311 that may be associated with and that may describe or otherwise beindicative of at least characteristic of one or more MCIs 310 of one ormore MCPs 305 of library 105. As a non-limiting example, a metadataasset can describe a characteristic associated with multiple MCIs 310(e.g., a metadata asset may be metadata 311 of two or more differentMCPs 305) in library 105. Each metadata asset can be represented as anode in metadata network 107 n. A metadata asset can be correlated withat least one other metadata asset. Each correlation between metadataassets can be represented as an edge in the metadata network that isbetween the nodes representing the correlated metadata assets.

Device 100 (e.g., processing circuitry 102) may include any suitableprocessing unit(s), such as one or more central processing units(“CPUs”), one or more graphics processing units (“GPUs”), otherintegrated circuits (“ICs”), memory, and/or other electronic circuitry.Such processing unit(s) may include any suitable MCP managementlogic/modules, which may be implemented as hardware (e.g., electroniccircuitry associated with processing circuitry 102, dedicated logic,etc.), software (e.g., one or more instructions associated with acomputer program (e.g., application 103) that may be executed byprocessing circuitry 102, software run on a general-purpose computersystem or a dedicated machine, etc.), or a combination thereof, forgenerating and/or maintaining and/or otherwise operating MCP managementsystem 107 by manipulating and/or otherwise processing any suitable dataof library 105 and any other suitable data (e.g., contextual data)available to device 100 (e.g., social media application data, contactapplication data, weather application data, health application data,calendar application data, messaging application data, e-mailapplication data, and/or the like). Therefore, device 100 and/or anyother portion(s) of system 1 (e.g., server 50) may be operative togenerate and use a knowledge graph metadata network 107 n as amulti-dimensional network, which may be a dynamically organizedcollection of metadata assets of metadata 311 of MCPs 305 of library 105but which may not include any media content items 310 of such MCPs 305,and/or which may be used for deductive reasoning. For example, device100 may be operative to (i) generate metadata network 107 n, (ii) relateand/or present at least two MCIs 310 based on metadata network 107 n,(iii) determine and/or present interesting MCIs 310 of library 105 basedon metadata network 107 n and predetermined criterion, (iv) selectand/or present representative MCIs 310 to summarize a collection (e.g.,a moment) of media content items based on input specifying therepresentative group's size, (v) use metadata network 107 n to reduce anumber of unverified persons detected in media content 310, (vi) usemetadata network 107 n to determine a mood of a collection (e.g., amoment) of media content items, and/or (vii) use metadata network 107 nto define a collection of media content items for a relevant interest.

Metadata network 107 n may enable deep connections between metadatausing multiple dimensions in the metadata network, which can betraversed for additionally deduced correlations. Each dimension in themetadata network may be viewed as a grouping of metadata based onmetadata type. For example, a grouping of metadata may be all timemetadata assets in a metadata collection (e.g., all metadata 311 oflibrary 105) and another grouping could be all geo-position metadataassets in the same metadata collection. Thus, in such an example, a timedimension may refer to all time metadata assets in the metadatacollection and a geo-position dimension may refer to all geo-positionmetadata assets in the same metadata collection. Furthermore, the numberof dimensions can vary based on constraints. Constraints may include,but are not limited to, a desired use for the metadata network, adesired level of detail, and/or the available metadata or computationalresources that may be used to implement the metadata network. Forexample, the metadata network can include only a time dimension, themetadata network can include all types of primitive metadata dimensions,and/or the like. With regard to the desired level of detail, eachdimension can be further refined based on specificity of the metadata.That is, each dimension in the metadata network may be a grouping ofmetadata based on metadata type and the granularity of information maybe described by the metadata. For a first example, there may be two timedimensions in the metadata network, where a first time dimension mayinclude all time metadata assets classified by week and a second timedimension may include all time metadata assets classified by month. Fora second example, there may be two geolocation dimensions in themetadata network, where a first geolocation dimension may include allgeolocation metadata assets classified by type of establishment (e.g.,home, business, etc.) and a second geolocation dimension that mayinclude all geolocation metadata assets classified by country. Thepreceding examples are merely illustrative and not restrictive. It is tobe appreciated that the level of detail for dimensions can varydepending on designer choice, application, available metadata, and/oravailable computational resources.

Metadata network 107 n may be a multi-dimensional network of MCPmetadata 311. As used herein, a “multi-dimensional network” and itsvariations may refer to a graph (e.g., a complex graph) having multiplekinds of relationships. A multi-dimensional network generally mayinclude multiple nodes and edges, where, in some embodiments, the nodesmay represent metadata and the edges may represent relationships orcorrelations between the metadata. Exemplary multi-dimensional networksinclude, but are not limited to, edge-labeled multigraphs, multipartiteedge-labeled multigraphs, DAGs, and multilayer networks. In someembodiments, the nodes in metadata network 107 n may represent metadataassets of MCP metadata 311, for example, where each node may represent aparticular metadata asset that may be associated with one or more MCIs310 and MCPs 305 of library 105 (e.g., a first node may be a firstmetadata asset that may not only be a part of first metadata 311associated with a first MCI 310 of a first MCP 305 of library 105 butthat may also be a part of second metadata 311 associated with a secondMCI 310 of a second MCP 305 of library 105. As another example, eachnode may represent a metadata asset that may be associated with a groupof MCIs in a collection. As used herein, a “metadata asset” and itsvariations may refer to metadata (e.g., a single instance of metadata, agroup of multiple instances of metadata, etc.) that may describe one ormore characteristics of one or more MCIs in a library. As such, theremay be a primitive metadata asset, an inferred metadata asset, and/orthe like. For a first example, a primary primitive metadata asset mayrefer to a time metadata asset describing a time interval between Jun.1, 2016 and Jun. 3, 2016 when one or more MCIs may have been captured.For a second example, a primary primitive metadata asset may refer to ageo-position metadata asset that may describe one or more latitudesand/or longitudes where one or more MCIs may have been captured. For athird example, an inferred metadata asset may refer to a time eventmetadata asset that may describe a holiday of Halloween.

Metadata network 107 n may be configured to include two types of nodes,such as moment nodes and non-moments nodes. As used herein, a “moment”may refer to a single event (e.g., as may be described by an event ormoment metadata asset) that may be associated with one or more MCIs. Forexample, a moment may refer to a vacation in Paris, France that lastedbetween Jun. 1, 2016 and Jun. 9, 2016 or to a Halloween party onHalloween afternoon at a person's home. For this example, the moment canbe used to identify one or more MCIs 310 (e.g., one image, a group ofimages, a video, a group of videos, a song, a group of songs, etc.) thatmay be associated with the vacation in Paris, France that lasted betweenJun. 1, 2016 and Jun. 9, 2016 or that may be associated with theafternoon Halloween party at a person's home. As used herein, a “momentnode” may refer to a node in a multi-dimensional network, such asmetadata network 107 n, that may represent a moment. Thus, a moment nodemay refer to a metadata asset (e.g., a primary inferred metadata asset)that may represent a single event or moment that may be associated withone or more MCIs. As used herein, a “non-moment node” may refer to anode in a multi-dimensional, such as metadata network 107 n, that maynot represent a moment. Thus, a non-moment node may refer to at leastone of a primary primitive metadata asset associated with one or moreMCIs or an inferred metadata asset associated with one or more MCIs thatis not a moment (i.e., not a moment metadata asset). As used herein, an“event” and its variations may refer to a situation or an activity thatmay be occurring at one or more locations during a specific timeinterval. An event may include, but is not limited to, one or more of agathering of one or more persons to perform an activity (e.g., aholiday, a vacation, a birthday, a dinner, a project, a work-outsession, etc.), a sporting event (e.g., an athletic competition, etc.),a ceremony (e.g., a ritual of cultural significance that is performed ona special occasion, etc.), a meeting (e.g., a gathering of individualsengaged in some common interest, etc.), a festival (e.g., a gathering tocelebrate some aspect in a community, etc.), a concert (e.g., anartistic performance, etc.), a media event (e.g., an event created forpublicity, etc.), a party (e.g., a large social or recreationalgathering, etc.), and/or the like. While network 107 n may be describedwith respect to moment nodes and non-moment nodes such that all nodesmay be related via a moment dimension (e.g., a time dimension, as eachmoment node may be associated with a discrete duration/range of time),network 107 n may alternatively be described with respect to “visit”nodes and non-visit nodes such that all nodes may be related via a visitdimension (e.g., a location dimension, where each visit node may beassociated with a discrete geographic location/range of locations, notbeholden to any particular time frame), or with respect to any othertype of nod/dimension(s) delineation.

Edges in metadata network 107 n between nodes may representrelationships or correlations between the nodes. For example, system 1may update metadata network 107 n as new metadata 311 is obtained.System 1 may be configured to manage MCIs 310 of library 105 usingmetadata network 107 n, such as to relate multiple MCIs based on thecorrelations (e.g., the edges in metadata network 107 n) betweenmetadata assets associated with the MCIs (e.g., the nodes of metadatanetwork 107 n). For example, a first group of one or more MCIs 310 maybe related to a second group of one or more MCIs based on the metadataassets that may be represented as moment nodes in metadata network 107n. As another example, metadata network 107 n may be used to identifyand present or otherwise utilize interesting groups of one or more MCIs310 in library 105 based on certain correlations (e.g., certain edges inmetadata network 105) between metadata assets associated with the MCIs(e.g., the nodes in metadata network 107 n) and any suitablepredetermined criterion, where the interesting groups of MCIs may beselected based on moment nodes in metadata network 107 n and suchpredetermined criterion may refer to any suitable contextualinformation. It is to be appreciated that metadata network 107 n of FIG.4 is exemplary and that every node that can be generated by system 1 isnot shown. For example, even though every possible node is notillustrated in FIG. 4 , system 1 may be operative to generate a node torepresent each metadata asset of library 105.

In metadata network 107 n of FIG. 4 , nodes representing metadata may beillustrated as boxes while edges representing correlations betweenmetadata may be illustrated as labeled connections between boxes.Furthermore, moment nodes (e.g., a first moment node 402, a secondmoment node 404, and a third moment node 470) may be represented asboxes with thickened boundaries while other non-moment nodes (e.g.,nodes 406-468) may lack such thickened boundaries. System 1 (e.g.,processing circuitry 102) may be operative to define nodes based onmetadata 311 associated with MCIs 310 of MCPs 305 of library 105, and,as additional metadata 311 is determined (e.g., as new metadata iscaptured, assigned, inferred, derived, and/or the like (e.g., asadditional MCIs 310 are captured or added to library 105 and/or asadditional explicit user actions are taken and/or as additionalcontextual data is made available to system 1), additional nodes and/oredges may be generated and added to metadata network 107 n.

As shown, metadata network 107 n may include a first moment metadataasset node 402 and a second moment metadata asset node 404. Any momentnode may be generated for a particular moment that may be identified bysystem 1 based on library 105 in any suitable manner. For example, whenat least a threshold amount of MCIs 310 are identified to be associatedwith time metadata within a particular time range and with locationmetadata within a particular location range, then those identified MCIs310 may be associated with a moment metadata asset that is descriptiveof that time range and location range (e.g., a moment that may beindicative of an interesting event that took place during that timerange at that location(s) due to at least a threshold amount of MCIsbeing captured). Alternatively, a particular subset of MCIs 310 oflibrary 105 may be associated with a particular moment metadata asset inany other suitable manner. As just one particular example, which may bereferred to herein with respect to metadata network 107 n of FIG. 4 ,first moment metadata asset node 402 may be defined to represent firstmoment metadata indicative of a first moment that may be based on aparticular time range of 2:00 PM to 4:00 PM on Oct. 31, 2009 and for aparticular location range (e.g., within 100 feet) of a particulargeographic coordinate (e.g., a particular address), such as a user'shome at 22 Skyline Drive in Wellesley, Mass., 02482, U.S.A., where sucha first moment may be defined as a result of at least a certain numberof MCIs 310 being identified in library 105 that are associated withtime metadata 311 indicative of any time within that time range and withlocation metadata 311 indicative of any location within that locationrange (e.g., when many MCIs are captured at a Halloween party at aperson's home), while second moment metadata asset node 404 may bedefined to represent second moment metadata indicative of a secondmoment that may be based on a particular time range of Jun. 30, 2016through Jul. 1, 2016 and for a particular location range (e.g., withinthe city limits of a particular city), such as within New York City,N.Y., U.S.A., where such a second moment may be defined as a result ofat least a certain number of MCIs 310 being identified in library 105that are associated with time metadata 311 indicative of any time withinthat time range and with location metadata 311 indicative of anylocation within that location range (e.g., when many MCIs are capturedduring a vacation to New York City), where each one of such MCIsassociated with second moment 404 may be different than each one of suchMCIs associated with first moment 402. Although only two moment nodesmay be shown in FIG. 4 , network 107 n may include more than two momentnodes, each associated with a particular moment of a particular timerange and a particular geographic range. Two moment nodes may becorrelated by advancement of time (e.g., second moment metadata assetnode 404 associated with the year 2016 may be after first momentmetadata asset node 402 associated with the year 2009, as may be shownby the edge labelled “Next” between nodes 402 and 404).

Any suitable nodes may be associated with any suitable metadata assetsand may be defined within network 107 n and correlated with one or moremoment nodes and/or one or more non-moment nodes. As shown, first momentmetadata asset node 402 may be correlated (e.g., by date) with at leastone time date metadata asset node 406 that may be defined to representtime date metadata indicative of a first date (e.g., Oct. 31, 2009)and/or may be correlated (e.g., by address) with at least one locationaddress metadata asset node 408 that may be defined to representlocation address metadata indicative of a first address (e.g., 22Skyline Drive, Wellesley, Mass., 02482, U.S.A. or an associatedgeographic coordinate system (e.g., latitude, longitude, and/oraltitude)). At least one MCI 310 of library 105 may be associated withfirst moment metadata represented by moment node 402 and time metadatarepresented by time node 406 and location metadata represented bylocation node 408. As shown, time date metadata asset node 406 (e.g.,indicative of Oct. 31, 2009) may be correlated (e.g., by season) with atime season metadata asset node 410 that may be defined to representtime season metadata indicative of a first season (e.g., Fall) and/ormay be correlated (e.g., by year) with a time year metadata asset node412 that may be defined to represent time year metadata indicative of afirst year (e.g., 2009) and/or may be correlated (e.g., by month) with atime month metadata asset node 414 that may be defined to represent timemonth metadata indicative of a first month (e.g., October) and/or may becorrelated (e.g., by day) with a time day metadata asset node 416 thatmay be defined to represent time day metadata indicative of a first day(e.g., 31) and/or may be correlated (e.g., by holiday) with a timeholiday metadata asset node 418 that may be defined to represent timeholiday metadata indicative of a first holiday (e.g., Halloween), and,although not shown, it is to be understood that time date metadata assetnode 406 may be correlated with any other suitable types of metadataasset nodes within network 107 n, including, but not limited to, a timeday of week metadata asset node that may be defined to represent timeday of week metadata indicative of a day of week (e.g., Saturday) and/orthe like. Additionally or alternatively, as shown, location addressmetadata asset node 408 (e.g., indicative of 22 Skyline Drive,Wellesley, Mass., 02482, U.S.A.) may be correlated (e.g., by city) witha location city metadata asset node 420 that may be defined to representlocation city metadata indicative of a first city (e.g., Wellesley),which may be correlated (e.g., by state) with a location state metadataasset node 422 that may be defined to represent location state metadataindicative of a first state (e.g., Massachusetts), which may becorrelated (e.g., by country) with a location country metadata assetnode 424 that may be defined to represent location country metadataindicative of a first country (e.g., United States of America), and,although not shown, it is to be understood that location addressmetadata asset node 408 may be correlated (e.g., directly or via anothernode) with any other suitable types of metadata asset nodes withinnetwork 107 n, including, but not limited to, a location ZIP codemetadata asset node that may be defined to represent location ZIP codemetadata indicative of a ZIP code and/or the like.

Additionally or alternatively, as shown, first moment metadata assetnode 402 may be correlated (e.g., by presence) with at least one personidentity metadata asset node, such as a person identity metadata assetnode 426 that may be defined to represent person identity metadataindicative of a first identity (e.g., John Doe) and/or a person identitymetadata asset node 428 that may be defined to represent person identitymetadata indicative of a second identity (e.g., Jane Doe) and/or aperson identity metadata asset node 430 that may be defined to representperson identity metadata indicative of a third identity (e.g., a firstunknown person), while person identity metadata asset node 428 may becorrelated (e.g., by spouse) with person identity metadata asset node426 (e.g., when the first identity (e.g., John Doe) and the secondidentity (e.g., Jane Doe) are determined to be each other's spouse). Atleast one MCI 310 of library 105 may be associated with first momentmetadata represented by moment node 402 and person metadata representedby person node 426, at least one MCI 310 of library 105 may beassociated with first moment metadata represented by moment node 402 andperson metadata represented by person node 428, and at least one MCI 310of library 105 may be associated with first moment metadata representedby moment node 402 and person metadata represented by person node 430.Network 107 n may also include a person social group metadata asset node432 that may be defined to represent person social group metadataindicative of a first social group and that may be correlated (e.g., bysocial group) with moment node 402, and each one of person identitymetadata asset node 426 and person identity metadata asset node 428 andperson identity metadata asset node 430 may be correlated (e.g., bybelonging) with person social group metadata asset node 432 (e.g., whenthe first identity (e.g., John Doe) and the second identity (e.g., JaneDoe) and the third identity (e.g., the first unknown person) aredetermined to be of a particular social group). Network 107 n may alsoinclude a location home metadata asset node 434 that may be defined torepresent location home metadata indicative of a first home orresidence, and location address metadata asset node 408 may becorrelated (e.g., by home) with location home metadata asset node 434(e.g., when the first address (e.g., 22 Skyline Drive, Wellesley, Mass.,02482, U.S.A.) is determined to be a home or residence), while each oneof person identity metadata asset node 426 and person identity metadataasset node 428 may be correlated (e.g., by residence) with location homemetadata asset node 434 (e.g., when each one of the first identity(e.g., John Doe) and the second identity (e.g., Jane Doe) is determinedto reside at the first home). Additionally or alternatively, as shown,first moment metadata asset node 402 may be correlated (e.g., bypresence) with at least one scene metadata asset node, such as a scenemetadata asset node 436 that may be defined to represent scene metadataindicative of a first scene (e.g., a dog). At least one MCI 310 oflibrary 105 may be associated with first moment metadata represented bymoment node 402 and scene metadata represented by scene node 436.

Second moment metadata asset node 404 may be correlated (e.g., by date)with at least one time date metadata asset node 438 that may be definedto represent time date metadata indicative of a second date (e.g., Jun.30, 2016) and/or may be correlated (e.g., by date) with at least oneother time date metadata asset node 440 that may be defined to representtime date metadata indicative of a third date (e.g., Jul. 1, 2016)and/or may be correlated (e.g., by address) with at least one locationaddress metadata asset node 442 that may be defined to represent atleast one location address metadata indicative of a second address(e.g., 350 5^(th) Avenue, New York, N.Y. 10118, U.S.A. or an associatedgeographic coordinate system (e.g., latitude, longitude, and/oraltitude)). At least one MCI 310 of library 105 may be associated withsecond moment metadata represented by moment node 404 and locationmetadata represented by location node 408 and at least one of timemetadata represented by time node 438 and time metadata represented bytime node 440. As shown, time date metadata asset node 438 (e.g.,indicative of Jun. 30, 2016) may be correlated (e.g., by season) with atime season metadata asset node 444 that may be defined to representtime season metadata indicative of a second season (e.g., Summer) and/ormay be correlated (e.g., by year) with a time year metadata asset node446 that may be defined to represent time year metadata indicative of asecond year (e.g., 2016) and/or may be correlated (e.g., by month) witha time month metadata asset node 448 that may be defined to representtime month metadata indicative of a second month (e.g., June) and/or maybe correlated (e.g., by day) with a time day metadata asset node 450that may be defined to represent time day metadata indicative of asecond day (e.g., 30), and, although not shown, it is to be understoodthat time date metadata asset node 438 may be correlated with any othersuitable types of metadata asset nodes within network 107 n, including,but not limited to, a time day of week metadata asset node that may bedefined to represent time day of week metadata indicative of a day ofweek (e.g., Thursday) and/or the like. Additionally or alternatively, asshown, time date metadata asset node 440 (e.g., indicative of Jul. 1,2016) may be correlated (e.g., by season) with time season metadataasset node 444 that may be defined to represent time season metadataindicative of the second season (e.g., Summer) and/or may be correlated(e.g., by year) with a time year metadata asset node 446 that may bedefined to represent time year metadata indicative of the second year(e.g., 2016) and/or may be correlated (e.g., by month) with a time monthmetadata asset node (not shown) that may be defined to represent timemonth metadata indicative of a third month (e.g., July) and/or may becorrelated (e.g., by day) with a time day metadata asset node (notshown) that may be defined to represent time day metadata indicative ofa third day (e.g., 1), and, although not shown, it is to be understoodthat time date metadata asset node 440 may be correlated with any othersuitable types of metadata asset nodes within network 107 n, including,but not limited to, a time day of week metadata asset node that may bedefined to represent time day of week metadata indicative of a day ofweek (e.g., Friday) and/or the like. Additionally or alternatively, asshown, location address metadata asset node 442 (e.g., indicative of 3505^(th) Avenue, New York, N.Y. 10118, U.S.A.) may be correlated (e.g., bycity) with a location city metadata asset node 452 that may be definedto represent location city metadata indicative of a second city (e.g.,New York City), which may be correlated (e.g., by state) with a locationstate metadata asset node 454 that may be defined to represent locationstate metadata indicative of a second state (e.g., New York), which maybe correlated (e.g., by country) with location country metadata assetnode 424 that may be defined to represent location country metadataindicative of the first country (e.g., United States of America), and,although not shown, it is to be understood that location addressmetadata asset node 442 may be correlated (e.g., directly or via anothernode) with any other suitable types of metadata asset nodes withinnetwork 107 n, including, but not limited to, a location ZIP codemetadata asset node that may be defined to represent location ZIP codemetadata indicative of a ZIP code and/or the like.

Additionally or alternatively, as shown, second moment metadata assetnode 404 may be correlated (e.g., by presence) with at least one personidentity metadata asset node, such as person identity metadata assetnode 426 that may be defined to represent person identity metadataindicative of the first identity (e.g., John Doe) and/or person identitymetadata asset node 428 that may be defined to represent person identitymetadata indicative of the second identity (e.g., Jane Doe) and/or aperson identity metadata asset node 456 that may be defined to representperson identity metadata indicative of a fourth identity (e.g., JennDoe) and/or a person identity metadata asset node 466 that may bedefined to represent person identity metadata indicative of a fifthidentity (e.g., a second unknown person). Although not shown, personidentity metadata asset node 456 may be correlated (e.g., by offspring)with person identity metadata asset node 426 and with person identitymetadata asset node 428 (e.g., when the fourth identity (e.g., Jenn Doe)is determined to be the offspring of the first identity (e.g., John Doe)and of the second identity (e.g., Jane Doe)). At least one MCI 310 oflibrary 105 may be associated with second moment metadata represented bymoment node 404 and person metadata represented by person node 426, atleast one MCI 310 of library 105 may be associated with second momentmetadata represented by moment node 404 and person metadata representedby person node 428, at least one MCI 310 of library 105 may beassociated with second moment metadata represented by moment node 404and person metadata represented by person node 456, and at least one MCI310 of library 105 may be associated with second moment metadatarepresented by moment node 404 and person metadata represented by personnode 466. Network 107 n may also include a person social group metadataasset node 458 that may be defined to represent person social groupmetadata indicative of a second social group and that may be correlated(e.g., by social group) with moment node 404, and each one of personidentity metadata asset node 426 and person identity metadata asset node428 and person identity metadata asset node 456 may be correlated (e.g.,by belonging) with person social group metadata asset node 458 (e.g.,when the first identity (e.g., John Doe) and the second identity (e.g.,Jane Doe) and the fourth identity (e.g., Jenn Doe) are determined to beof a particular social group (e.g., a family)), while it is to be notedthat person identity metadata asset node 466 may not be correlated withperson social group metadata asset node 458 (e.g., when the fifthidentity (e.g., second unknown person) is determined not to be of aparticular social group (e.g., a family)). However, network 107 n mayalso include a person social group metadata asset node 468 that may bedefined to represent person social group metadata indicative of a thirdsocial group and that may be correlated (e.g., by social group (notshown)) with moment node 402, and each one of person identity metadataasset node 456 and person identity metadata asset node 466 may becorrelated (e.g., by belonging) with person social group metadata assetnode 468 (e.g., when the fourth identity (e.g., Jenn Doe) and the fifthidentity (e.g., second unknown person) are determined to be of aparticular social group). Network 107 n may also include a location areametadata asset node 460 that may be defined to represent location areametadata indicative of a first area (e.g., an area of interest), such asthe Empire State Building, and location address metadata asset node 442may be correlated (e.g., by area) with location area metadata asset node460 (e.g., when the second address (e.g., 350 5^(th) Avenue, New York,N.Y. 10118, U.S.A.) is determined to be a particular area (e.g., an areaof interest)). Additionally or alternatively, as shown, second momentmetadata asset node 404 may be correlated (e.g., by point of interest(“POI”)) with at least one place POI metadata asset node, such as aplace POI metadata asset node 462 that may be defined to represent placePOI metadata indicative of a first POI (e.g., culture). At least one MCI310 of library 105 may be associated with second moment metadatarepresented by moment node 404 and place POI metadata represented byplace POI node 462. Additionally or alternatively, as shown, secondmoment metadata asset node 404 may be correlated (e.g., by region ofinterest (“ROI”)) with at least one place ROI metadata asset node, suchas a place ROI metadata asset node 464 that may be defined to representplace ROI metadata indicative of a first ROI (e.g., urban). At least oneMCI 310 of library 105 may be associated with second moment metadatarepresented by moment node 404 and place ROI metadata represented byplace ROI node 464. Additionally or alternatively, as shown, secondmoment metadata asset node 404 may be correlated (e.g., by presence)with at least one scene metadata asset node, such as scene metadataasset node 436 that may be defined to represent scene metadataindicative of the first scene (e.g., a dog). At least one MCI 310 oflibrary 105 may be associated with second moment metadata represented bymoment node 404 and scene metadata represented by scene node 436.

It is to be understood that FIG. 4 is just exemplary of what may only bea portion of one illustrative metadata network 107 n of system 1. Forexample, any node of FIG. 4 may be correlated with one or more othernodes of network 107 n not shown in FIG. 4 . For example, personidentity metadata node 426 may be correlated (not shown) with one ormore additional moment metadata nodes of network 107 n in addition tomoment nodes 402 and 404 (e.g., a third moment node 470 that may berepresentative of third moment metadata indicative of a third momentafter the second moment represented by moment node 404). Additionally oralternatively, location country metadata node 424 may be correlated withone or more additional location nodes of network 107 n in addition tolocation nodes 422 and 454. Additionally or alternatively, moment node404 may be correlated with one or more additional location nodes ofnetwork 107 n in addition to location node 442 (e.g., one or more MCIs310 of library 105 may be associated with second moment metadatarepresented by moment node 404 and location address metadata other thanthe location address represented by location address node 442 (e.g.,another MCI 310 associated with moment node 404 may be associated withthird location address metadata indicative of a third location addressother than 350 5^(th) Avenue, New York, N.Y. 10118, (e.g., an addressassociated with another area of interest other than the Empire StateBuilding))). Network 107 n may be further populated with one or moreadditional nodes and/or one or more additional edges between nodes whenone or more new metadata assets may be determined and associated withone or more MCIs already associated with moment metadata represented byone of moment node 402 and moment 404 and/or when one or more new MCIsmay be associated with moment metadata represented by one of moment node402 and moment node 404 and/or when further processing is carried out bysystem 1 on any existing metadata and/or contextual data available tosystem 1. It is to be appreciated that certain metadata assets and theirrespective nodes may be associated with individual MCIs and/or withother nodes (e.g., moment nodes) that may be associated with individualMCIs or groups of MCIs. For example, a moment node may be associatedwith each MCI in a group of MCIs, where each MCI in the group of MCIsmay be associated with time metadata indicative of a time within thetime range of the moment and/or may be associated with location metadataindicative of a location within the location range of the moment. Asanother example, a scene node (e.g., scene metadata asset node 436 thatmay be defined to represent scene metadata indicative of a first scene(e.g., a dog)) may be correlated with moment node 402, which may beassociated with each MCI that has time and/or location metadata relatedto the time and location constraints of the moment, while only one orsome but not all of those MCIs may be associated with scene metadataindicative of the scene represented by the scene node. Therefore, ascene node may be correlated to a moment node that is associated with agroup of MCIs, while the scene node may also be directly associated withonly one or some (or maybe all) of the MCIs of that group.

System 1 may be configured to generate additional nodes based on momentnodes in any suitable manner, including, but not limited to, determining(e.g., detecting, receiving, inferring, deriving, or otherwise obtaininga new metadata asset associated with a moment node by cross-referencingthe new metadata asset with other assets in network 107 n and/orgenerating a node for each metadata asset of library 105. System 1 maybe configured to refine one, some, or each metadata asset associatedwith a moment nodes in any suitable manner, such as based on aprobability distribution (e.g., a discrete probability distribution, acontinuous probability distribution, etc.). For example, a Gaussiandistribution may be used to determine a distribution of at least somemetadata assets, such as the primary primitive metadata assets. For thisexample, the distribution may be used to ascertain a mean, a median, amode, a standard deviation, a variance, and/or any other suitablecharacteristic associated with the distribution of the primary primitivemetadata assets. System 1 may be configured to use the Gaussiandistribution to select or filter out a subset of the primary primitivemetadata assets that may be within any suitable predetermined criterion(e.g., 1 standard deviation (e.g., 68%), 2 standard deviations (e.g.,95%), 3 standard deviations (e.g., 99.7%), etc.). Hence, suchselection/filtering operation(s) may be operative to assist withidentifying relevant primary primitive metadata assets for MCPmanagement and/or with filtering out noise and/or unreliable primaryprimitive metadata assets. Consequently, other types of metadata (e.g.,inferred metadata assets, etc.) that may be associated with, determinedfrom, or inferred from the primary primitive metadata assets may also berelevant and relatively noise-free. As another example, a Gaussiandistribution may be used to determine a distribution of one, some, oreach moment nodes. For this example, the distribution may be used toascertain a mean, a median, a mode, a standard deviation, a variance,and/or any other suitable characteristic associated with thedistribution of the moments. System 1 may be operative to use theGaussian distribution to select or filter out a subset of the momentnodes that may be within any suitable predetermined criterion (e.g., 1standard deviation (e.g., 68%), 2 standard deviations (e.g., 95%), 3standard deviations (e.g., 99.7%), etc.). Hence, suchselection/filtering operation(s) may be operative to assist withidentifying relevant moment nodes for MCP management and/or withfiltering out noise and/or unreliable primary inferred metadata assetsor otherwise. Consequently, other types of metadata (e.g., primaryprimitive metadata assets, auxiliary inferred metadata assets, etc.)that may be associated with, determined from, or extracted from themoment metadata assets may also be relevant and relatively noise-free.Noise may occur due to primary primitive metadata assets that may beassociated with one or more irrelevant MCIs, where such MCIs may bedetermined based on the number of MCIs associated with a primaryprimitive metadata asset. For example, a primary primitive metadataasset associated with two or less MCIs may be designated as noise. Thismay be because such metadata assets (and their associated MCIs) may beirrelevant given the little information they provide. For example, themore important or significant an event is to a user, the higher thelikelihood that the event is captured using a large number of MCIs(e.g., three or more, etc.). For this example, the probabilitydistribution described above may enable selecting the primary primitivemetadata asset associated with these MCIs. This may be because thenumber of MCIs associated with the event may suggest an importance orrelevance of the primary primitive metadata asset. In contrast,insignificant events may have only one or two MCIs captured, and thecorresponding primary primitive metadata asset may not add much to MCPmanagement based on the use of a metadata network, for example. Theimmediately preceding examples may also be applicable to any types ofmetadata.

System 1 may be configured to determine a correlation weight (e.g.,confidence weight and/or a relevance weight) for one, some, or eachmetadata asset and/or one, some, or each correlation between any twometadata nodes representative of any two metadata assets. As usedherein, a “confidence weight” and its variations may refer to a value(e.g., an integer, etc.) that may be used to describe a certainty that ametadata asset correctly identifies a feature or characteristic of oneor more MCIs (e.g., one or more MCIs associated with a moment). Forexample, a confidence weight of 0.6 (e.g., out of a maximum of 1.0) canbe used to indicate a 60% confidence level that a feature (e.g., ascene) in one or more MCIs associated with a moment is a dog. As usedherein, a “relevance weight” and its variations may refer to a value(e.g., an integer, etc.) that may be used to describe an importanceassigned to a feature or characteristic of one or more MCIs (e.g., oneor more MCIs associated with a moment) as identified by a metadataasset. For example, a first relevance weight of 0.85 (e.g., out of amaximum of 1.0) can be used to indicate that a first identified featurein an MCI (e.g., a person) is very important while a second relevanceweight of 0.50 (e.g., out of a maximum of 1.0) can be used to indicatethat a second identified feature in an MCI (e.g., a dog) is not asimportant.

As shown in FIG. 4 , for example, system 1 may be operative to estimatethat one or more metadata assets associated with one or more MCIsassociated with moment node 402 describe a dog. For this example, acorrelation weight of a correlation 435 between node 402 and node 436may be assigned a value 0.8, which may be any suitable confidenceweight, any suitable relevance weight, or any suitable combination ofany suitable confidence weight and any suitable relevance weight (e.g.,a confidence weight (“C-weight”) may be determined to be a value of 0.9to indicate a 90% confidence level that a scene dog metadata asset ofscene dog metadata asset node 436 is or ought to be associated withmoment node 402 (e.g., with one or more MCIs associated with the firstmoment represented by moment node 402) and a relevance weight(“R-weight”) may be determined to be a value of 0.7 to indicate that ascene dog metadata asset of scene dog metadata asset node 436 is arelatively important feature of moment node 402 (e.g., of one or moreMCIs associated with the first moment represented by moment node 402),such that a correlation weight (“weight”) may be an average value of 0.8(alternatively, a correlation weight may be based on a differencebetween a confidence weight and a relevance weight for a particularedge)). With specific regard to scene metadata assets and/or personidentity metadata assets associated with any suitable image MCIs,correlation weights and/or confidence weights and/or relevance weightsmay be detected via any suitable feature detection techniques that mayinclude analyzing such metadata associated with one or more MCIs. Forexample, system 1 may be configured to determine any suitable weight(s)using metadata associated with one or more MCIs by applying knownfeature detection techniques. Relevance can be statically defined in ametadata network from external constraints. For example, relevance canbe based on information (e.g., contextual information) that may beacquired from any suitable sources, such as social network data,calendar data, and/or the like. Additionally or alternatively, relevancemay be based on any suitable internal constraints, where, for example,as more detections of a metadata asset are made, its relevance can beincreased. Relevance may also retard as fewer detections are made. Forexample, as more detections of the second identified person metadataassociated with person metadata node 428 (e.g., Jane Doe) are made overa predetermined period of time (e.g., an hour, a day, a week, a year,etc.), that person's relevance may be increased to indicate thatperson's importance (e.g., to a user of system 1 (e.g., to John Doe)).Confidence can be dynamically generated based on the ingest of anysuitable metadata in a metadata network. For instance, a detected personin an MCI may be linked with information (e.g., contextual information)about that person as may be obtained from a contacts application, acalendar application, a social networking application, and/or any othersuitable source to determine a level of confidence that the detectedperson is correctly identified. For a further example, an overalldescription of a scene in an MCI may be linked with geo-positioninformation that may be acquired from metadata associated with the MCIto determine the level of confidence. Many other examples are possible.In addition, confidence can be based on any suitable internalconstraints, where, for example, as more detections of a metadata assetare made, its identification confidence may be increased. Confidence canalso retard as fewer detections are made.

FIG. 6 is a flowchart of an illustrative process 600 for operatingsystem 1 (e.g., a process that may be performed by media managementsystem 500 of FIG. 5 ) for defining a collection of media content itemsof a media library for a relevant interest (e.g., for identifying asubset of MCIs of a media library, where each MCI of the subset isassociated with an interest (e.g., hobby or avocation or concernment)that has been determined to be of some relevance). Process 600 may useany suitable metadata (e.g., any suitable metadata network and/or anysuitable contextual data) that may be associated with the media libraryin any suitable manner in order to define a collection of media contentitems for any suitable interest. Process 600 may include (i) identifyinga person identity associated with the media library (e.g., using anysuitable identity metadata for a particular identity (e.g., a verifiedor unverified identity) or the like), (ii) identifying a locationintimately associated with the person identity (e.g., a home or officeor any other suitable type of location frequently associated with theperson identity (e.g., using any suitable location metadata and/orcorrelation information for a particular location with respect to theperson identity or the like)), (iii) identifying an interest that isassociated in a particular manner with the identified location (e.g., aninterest that is associated with at least 3 moments that are associatedwith the identified location (e.g., using any suitable interest metadata(e.g., scene data) and/or correlation information for a particularinterest (e.g., an activity, an animal, an instrument, a baby, etc.)with respect to the identified location or the like)), and (iv)identifying each MCI of the media library that is associated with theperson identity and with the interest (e.g., identifying each momentthat may be associated with both the person identity and the interest,and then identifying from the MCIs associated with such moments, eachMCI associated with the hobby). Then a collection may be defined toinclude those identified MCIs, and the collection may be used in anysuitable manner, such as to provide an album or a composite presentationbased on at least some of the MCIs of the defined collection. A majorityor the entirety of process 600 may be carried out without any userinteraction (e.g., transparent to a user of system 1), which, forexample, may reduce the cognitive burden on a user and/or avoid anytedious classification of media by a user, thereby creating a moreefficient human-machine interface.

At operation 602 of process 600, one or more processors of the systemmay select a person identity associated with a media library. Forexample, any suitable person identity associated with media library 105may be selected, such as the first verified identity John Doe that maybe indicated by person identity metadata that may be represented byperson identity metadata asset node 426 of metadata network 107 n or thefirst unverified unknown identity that may be indicated by personidentity metadata that may be represented by person identity metadataasset node 430 of metadata network 107 n, where the person identity maybe selected in any suitable manner, such as automatically by system 1based on any suitable information (e.g., based on the prominence of theidentity with respect to media library 105 (e.g., by analyzing themetadata associated with media library 105)) or through user-selection(e.g., a user may select (e.g., via any suitable system user interface(“UI”) (e.g., I/O component 109 a)) any suitable person identityassociated with media library 105).

At operation 604 of process 600, one or more processors of the systemmay determine if there is at least one location intimately associatedwith the selected person identity and, if so, process 600 may advance tooperation 606 of process 600, otherwise, process 600 may return tooperation 602 where a different person identity may be selected. Adetermination of operation 604 as to whether one or more locations maybe intimately associated with the person identity selected at operation602 may be made in any suitable manner, such as through processing anysuitable metadata associated with media library 105 (e.g., any explicitand/or inferred metadata and/or contextual data). For example, as shownin FIG. 4 , analysis of metadata network 107 n may be operative todetermine at operation 604 that, when the first person identityrepresented by person identity metadata asset node 426 of metadatanetwork 107 n is selected at operation 602, person identity metadataasset node 426 may be intimately associated with location home metadataasset node 434 that may be defined to represent location home metadataindicative of a first home or residence, and location address metadataasset node 408 may be correlated (e.g., by home) with location homemetadata asset node 434. An intimate association between a location anda person identity may be indicative of the person identity living at thelocation (e.g., place of residence (e.g., primary residence or secondaryresidence (e.g., summer home)) and/or may be indicative of the personidentity working at the location (e.g., place of business) and/or may beindicative of the person identity having any other suitable strongrelationship to the location (e.g., the location may be the home of theperson identity's parent or child). Two or more intimate locations maybe identified at operation 604 with respect to a selected personidentity (e.g., a primary residence and a summer residence).Alternatively, in some embodiments, only determination of a primaryresidence of the selected person identity may be operative to satisfyoperation 604.

At operation 606 of process 600, one or more processors of the systemmay determine if there is at least a threshold number of momentsassociated with both an interest and any location intimately associatedwith the selected person identity and, if so, process 600 may advance tooperation 608 of process 600, otherwise, process 600 may return tooperation 602 where a different person identity may be selected. Adetermination of operation 606 as to whether at least a threshold numberof moments may be associated with not only a particular interest butalso any location identified at operation 604 may be made in anysuitable manner, such as through processing any suitable metadataassociated with media library 105. For example, as shown in FIG. 4 ,analysis of metadata network 107 n may be operative to determine atoperation 606 that, when the first person identity represented by personidentity metadata asset node 426 of metadata network 107 n is selectedat operation 602 and at least the location address represented bylocation address metadata asset node 408 is determined at operation 604to be intimately associated with that selected person identity (e.g.,via location home metadata asset node 434), at least the first momentrepresented by first moment metadata asset node 402 may be not onlycorrelated (e.g., by location) with location address metadata asset node408 but also correlated (e.g., by presence) with scene metadata assetnode 436 that may be representative of a first interest (e.g., firstscene (e.g., a dog)). If the threshold number of moments of operation606 is defined to be the number 1, then this correlation of moment node402 with both location address node 408 and interest scene dog node 436may be enough to satisfy operation 606. However, if the threshold numberof moments of operation 606 is defined to be the number 2, then at leastone other moment (i.e., a moment different than the first moment ofmoment node 402) would have to be identified at operation 606 as beingassociated with not only interest scene dog node 436 but also with anylocation identified at operation 604 (e.g., the same location of addressnode 408 associated with interest scene dog node 436 and the firstmoment, or any other location identified at operation 604 (e.g., ifoperation 604 identified two or more locations intimately associatedwith the selected person identity (e.g., a primary residence and asummer home))) in order to satisfy operation 606. That is, in order tosatisfy the condition of operation 606, at least a threshold number ofmoments of media library 105 must be identified, where each of thosemoment must be associated with the same particular interest, and whereeach of those moment must be associated with any location identified atoperation 604 (e.g., a first of the identified moments may be associatedwith interest I and location X, while a second of the identified momentsmay be associated with interest I and location Y, and while a third ofthe identified moments may be associated with interest I and location Z,as long as each one of locations X, Y, and Z were identified atoperation 604 as being intimately associated with the person identityselected at operation 602).

The threshold number of moments of operation 606 may be defined as anysuitable number in any suitable manner. For example, the thresholdnumber of moments of operation 606 may be the number 1, the number 2,the number 3, the number 4, or the number 5. Alternatively, thethreshold number of moments of operation 606 may be a percentage of thetotal number of moments of the media library that may be associated withthe particular interest (e.g., the first scene (e.g., a dog) of scenenode 436), a percentage of the total number of moments of the medialibrary that may be associated with any location identified at operation604, a percentage of the total number of moments of the media library,and/or the like, where the percentage may be any suitable number, suchas 5%, 10%, 20%, 30%, 40%, 50%, or the like. Alternatively oradditionally, the threshold number may be determined using any suitablecalculation based on the distribution of the MCIs (e.g., using standarddeviation). Therefore, operation 606 may be satisfied if a particularinterest is determined to be associated with at least a particularnumber of moments also associated with a location that is intimatelyassociated with a particular person identity, such that operation 606may only be satisfied for an interest that is associated with threedifferent time ranges at a particular person identity's intimatelocation(s), thereby providing process 600 with enough confidence tohandle the interest as a relevant interest for that person identity.

The particular interest of operation 606 may be any suitable type ofinterest, including one or more particular types of entities that may berepresented by any suitable scene metadata assets and, thus any suitablescene metadata asset nodes (e.g., node 436 of network 107 n), or by anyother suitable type of metadata of media library 105, where the type ofinterest may include, but is not limited to, any suitable hobby, pet(e.g., dog (generally or a specific breed (e.g., German Shepherd,Retriever, Spaniel, etc.)), cat, bird, etc.), instrument (e.g.,generally or piano, guitar, violin, etc.), activity (e.g., generally orcooking, painting, video games, jacuzzi, basketball, boxing, juggling,magic, dancing, billiards, etc.), baby (e.g., a person object that maytypically not be associated with a particular identity (e.g., verifiedor unverified)), and/or the like, where, as mentioned, scene metadatamay be indicative of an overall description of an activity or situationassociated with one or more MCIs based on any objects that may bedetected therein (e.g., if a MCI includes a group of images, then scenemetadata for the group of images may be determined using detectedobjects in one or more of the images (e.g., the detection of a largecake with candles and/or balloons in at least two images in the groupcan be used to associate “birthday” scene metadata with each of theimages)), where such objects or scene indicators or content indicatorsmay be any suitable objects (e.g., a detected animal, a detected companylogo, a detected piece of furniture, a detected instrument, etc.) thatmay be able to be detected in a media content item using any suitabletechniques (e.g., any suitable image processing techniques), which maybe metadata 350 of media library 105 and/or any suitable node(s) ofnetwork 107 n. The particular interest of operation 606 may be of anysuitable granularity, such as an “animal” or an “animal mammal” or an“animal mammal canine” or an “animal mammal canine dog” or an “animalmammal canine dog shepherd” or an “animal mammal canine dog shepherdgerman” or the like, but the same level of granularity may be used foreach one of the threshold number moments to satisfy operation 606.Operation 606 may be configured to satisfy the condition of operation606 by first analyzing a first particular interest with respect to thelocation(s) of operation 604, where the first particular interest may bethe most prevalent interest of media library 105, and if that analysisdoes not satisfy operation 606, then the next most prevalent interestmay be analyzed, and so on until an interest is identified thatsatisfies operation 606 for the location(s) of operation 604 such thatprocess 600 may advance to operation 608, otherwise process 600 mayreturn to operation 602 and select a different person identity. In someembodiments, a particular interest to be used at operation 606 may beselected in any suitable manner (e.g., via any suitable UI) by a user orin response to any suitable action (e.g., a user tagging a “dog” in oneor more MCIs (e.g., any suitable explicit user metadata 330)) or in anyother suitable manner.

At operation 608 of process 600, one or more processors of the systemmay identify each moment of library 105 that may be associated with notonly a particular person identity (e.g., the person identity selected atoperation 602) but also a particular interest (e.g., the particularinterest satisfying operation 606 (e.g., with respect to any location(s)intimately associated with the person identity)). Therefore, in someembodiments, operation 608 may identify not only each one of the momentsused to satisfy operation 606 (e.g., at least a threshold number ofmoments associated not only with the particular interest but also anylocation intimately associated with the selected person identity) butalso any other moments that may be associated with the particularinterest and with the selected person identity but that may notnecessarily be associated with a location intimately associated with theselected person identity. The identification of moments of operation 608may be made in any suitable manner, such as through processing anysuitable metadata associated with media library 105. For example, asshown in FIG. 4 , analysis of metadata network 107 n may be operative toidentify at operation 608 that, when the first person identityrepresented by person identity metadata asset node 426 of metadatanetwork 107 n is selected at operation 602 and when the particularinterest satisfying operation 606 is the first scene (e.g., a dog)represented by scene metadata asset node 436 of metadata network 107 n,at least each one of the first moment represented by first momentmetadata asset node 402 and the second moment represented by secondmoment metadata asset node 404 may be associated (e.g., by presence) notonly with person identity metadata asset node 426 of the selected personidentity but also with scene metadata asset node 436 of the particularinterest.

At operation 610 of process 600, one or more processors of the systemmay determine whether any identified moment (e.g., any moment identifiedat operation 608) is a valuable interest moment, where an identifiedmoment is a valuable interest moment when each MCI of at least athreshold percentage of the MCIs associated with the identified momentis associated with the particular interest, and, if so, process 600 mayadvance to operation 612 of process 600, otherwise, process 600 mayreturn to operation 606 where a different particular interest may beidentified to attempt to satisfy the condition of operation 606 (or mayreturn from operation 606 to operation 602 if such a differentparticular interest is not available). A determination of operation 610as to whether a moment identified at operation 608 is a valuableinterest moment may be made in any suitable manner, such as throughprocessing any suitable metadata associated with media library 105. Forexample, operation 610 may be operative to analyze metadata associatedwith particular MCIs of an identified moment to determine how many MCIsof the identified moment are directly associated (e.g., not only via theidentified moment) with metadata indicative of the particular interestand then determine whether that number of MCIs is at least a thresholdpercentage of the total number of MCIs of the identified moment. Asmentioned, each MCI of a moment may be directly associated with timemetadata indicative of a time within a time range of the moment anddirectly associated with location metadata indicative of a locationwithin a location range of the moment, such that the MCI may beassociated with the moment. Moreover, when a non-moment node may beassociated with a moment node, the non-moment node may be representativeof metadata that may be directly associated with only one or some (and,in some situations, all) of the MCIs associated with the moment. Forexample, in some embodiments, while scene metadata asset node 436representative of scene metadata indicative of the particular interest(e.g., the first scene (e.g., a dog)) may be correlated with moment node402, which may be associated with each MCI that has time and locationmetadata related to the time and location constraints of the moment,only one or some but not all of those MCIs may be associated with scenemetadata indicative of the particular interest (e.g., the first scene(e.g., a dog)) represented by scene metadata asset node 436 (e.g., only6 MCIs out of the 10 total MCIs that may be associated with moment node402 may be associated with scene metadata indicative of the particularinterest (e.g., only 60% of the MCIs associated with the first momentmay also be associated with the particular interest (e.g., each MCI 310of only 60% of the MCIs 310 associated with the first moment may be ofan MCP 305 with metadata 311 indicative of the particular interest(e.g., each MCI 310 of only 60% of the MCIs 310 associated with thefirst moment may be detected to include pixel data indicative of adog)))). None, one, some, or each of the moments identified at operation608 may be determined to be a valuable interest moment at operation 610.

The threshold percentage of operation 610 may be defined as any suitablenumber in any suitable manner. For example, the threshold percentage ofMCIs of operation 610 may be 30%, 40%, 50%, 60%, 70%, 80%, or the like.Alternatively, the threshold percentage of operation 610 may vary basedon the number of moments identified at operation 608 (e.g., thethreshold percentage may be greater when more moments are identified,and may be smaller when less moments are identified, or vice versa).Alternatively, the threshold percentage may not be a percentage but mayinstead be a number, such as 5, or 10, or 20, or the like. In oneparticular embodiment, the threshold percentage of operation 610 may be60%, while the threshold number of operation 606 may or may not be 3.Alternatively or additionally, rather than a threshold percentage, anysuitable threshold number may be used at operation 610 as may bedetermined using any suitable calculation based on the distribution ofthe MCIs (e.g., using standard deviation). Therefore, operation 610 maybe satisfied if at least one moment identified at operation 608 isassociated with a number of MCIs of which at least a particularthreshold percentage are associated with the particular interest (e.g.,each MCI of at least 60% of the MCIs of an identified moment must beassociated with the particular interest in order for that identifiedmoment to be considered a valuable interest moment), thereby providingprocess 600 with enough confidence in the relevancy of that moment withrespect to the particular interest. In some embodiments, operation 610may also provide one or more additional conditions on whether anidentified moment may be determined to be a valuable interest moment.For example, in addition to determining that an identified moment may bea valuable interest moment when each MCI of at least a thresholdpercentage of the MCIs associated with the identified moment isassociated with the particular interest, operation 610 may also beconfigured to determine whether each MCI of at least another thresholdpercentage of the MCIs associated with the identified moment isassociated with the selected person identity. As just one example, anidentified moment may only be determined by operation 610 to be avaluable interest moment if not only at least X % of the moment's MCIsare associated with the particular interest but also at least Y % of themoment's MCIs are associated with the selected person identity (e.g.,where Y % may be any suitable percentage greater than or less than X %(e.g., 60%)). As just one other example, an identified moment may onlybe determined by operation 610 to be a valuable interest moment if notonly at least X % of the moment's MCIs are associated with theparticular interest but also at least Y % of those MCIs of the X % areassociated with the selected person identity (e.g., where Y % of X % ofthe MCIs of the identified moment must be associated with not only theparticular interest but also the selected person identity).

At operation 612 of process 600, one or more processors of the systemmay identify, from each valuable interest moment, each MCI that isassociated with the particular interest (e.g., each MCI of at least thethreshold percentage of the total number of MCIs of each valuableinterest moment as associated with the particular interest). Therefore,operation 612 may be operative to identify all MCIs associated with theparticular interest that may also be MCIs associated with a valuableinterest moment that has been determined to be a relevant moment withrespect to the particular interest.

At operation 614 of process 600, one or more processors of the systemmay determine if there is at least a threshold number of identified MCIs(e.g., if the number of MCIs identified at operation 612 is at least athreshold number) and, if so, process 600 may advance to operation 616of process 600, otherwise, process 600 may return to operation 606 wherea different particular interest may be identified to attempt to satisfythe condition of operation 606 (or may return from operation 606 tooperation 602 if such a different particular interest is not available).The threshold number of identified MCIs of operation 614 may be definedas any suitable number in any suitable manner. For example, thethreshold number of identified MCIs of operation 616 may be 5, 10, 20,30, or the like. In some embodiments, the determination of operation 616may be done for multiple ranges of time, such as is there at least athreshold number of identified MCIs for a first period of time (e.g.,the year 2015 (e.g., is there at least a threshold number of theidentified MCIs associated with metadata indicative of the year 2015))and is there a threshold number of identified MCIs for a second periodof time (e.g., the year 2016), and if not, then a determination may bemade to determine if there is at least a threshold number of identifiedMCIs for a different period of time (e.g., the years 2015 and 2016combined). Therefore, operation 616 may be satisfied if at least aparticular number of identified MCIs are determined for at least onesuitable period of time (e.g., a month, a year, two years, or the entiretime span of library 105, etc.), such that operation 616 may only besatisfied for a particular interest that is associated with at least aparticular number of MCIs, where each MCI is also associated with amoment with at least a particular amount of relevancy with respect tothe particular interest.

At operation 616 of process 600, one or more processors of the systemmay define a collection of MCIs using at least a portion of theidentified MCIs (e.g., a collection of MCIs that may include at leastsome of the MCIs identified at operation 612). In some embodiments, asingle collection may be defined at operation 616 that may include someor most or all of the MCIs identified at operation 612. Alternatively,if operation 614 may determine that a first set of at least thethreshold number of identified MCIs are associated with a first timeframe and that a second set of at least the threshold number ofidentified MCIs are associated with a second time frame, then operation616 may define a first MCI collection that includes at least a portionor most or all of the first set of identified MCIs and may also define asecond MCI collection that includes at least a portion or most or all ofthe second set of identified MCIs. Any MCI collection of MCIs defined atoperation 616 may then be used in any suitable manner (e.g., forpresentation to a user as an album of MCIs or as a compositepresentation based on the MCIs of the collection), where the collectionof MCIs may be indicative of MCIs associated with an interest of somerelevancy. In some embodiments, the collection may be named or otherwiseassociated with textual information indicative of the selected personidentity and the particular interest that resulted in the collectionbeing defined, such as “John Doe's Dog” when the selected personidentity was the first person identity of node 426 and the particularinterest was the scene dog of node 436.

It is understood that the operations shown in process 600 of FIG. 6 areonly illustrative and that existing operations may be modified oromitted, additional operations may be added, and the order of certainoperations may be altered. In some embodiments, a particular interestmay be associated with one or more elements that may be considered toosimilar to the particular interest in one or more ways such that eachelement may be considered a blacklist element for the particularinterest, wherein a moment and/or MCI associated with both theparticular interest and a blacklist element for the particular interestmay be ignored with respect to one or more operations of process 600.For example, continuing with the example of a particular interest beinga “dog” (e.g., the scene dog of node 436), a blacklist element for a dogmay be a “baby,” as a baby may be often falsely detected as a dog and/ora dog may be often falsely detected as a baby (e.g., due to one or moreshortcomings in image processing and/or object recognition technology).In such an embodiment, operation 606 may be operative to determinewhether or not there is at least a threshold number of moments that isnot only (i) associated with the particular interest but also (ii)associated with any location intimately associated with the selectedperson identity and also (iii) not associated with any blacklist elementof the particular interest. Additionally or alternatively, in such anembodiment, operation 608 may be operative to identify each moment thatis not only (i) associated with the particular interest but also (ii)associated with the selected person identity and also (iii) notassociated with any blacklist element of the particular interest.Additionally or alternatively, in such an embodiment, operation 610 maybe operative to determine that an identified moment is a valuableinterest moment when each MCI of at least a threshold percentage of thetotal number of MCIs associated with the identified moment is not only(i) associated with the particular interest but also (ii) not associatedwith any blacklist element of the particular interest. Additionally oralternatively, in such an embodiment, operation 612 may be operative toidentify, from each valuable interest moment, each MCI that is not only(i) associated with the particular interest but also (ii) not associatedwith any blacklist element of the particular interest. Such handling ofone, some, each, or any blacklist elements of a particular interest maybe operative to increase confidence in the MCIs that may be identifiedfor defining a collection of MCIs for the particular interest.

Operation 604 may be omitted in some embodiments of process 600, wherebyprocess 600 may advance from operation 602 to operation 606 whenoperation 602 is satisfied. In such embodiments, operation 606 may beoperative to determine whether or not there is at least a thresholdnumber of moments that is not only (i) associated with the particularinterest but also (ii) associated with the selected person identity(e.g., generally associated with the selected person identity withoutany condition with respect to any location (e.g., a location that may beintimately associated with the selected person identity)) and, in someembodiments, also (iii) not associated with any blacklist element of theparticular interest.

Operations 604 and 606 may be omitted in some embodiments of process600, whereby process 600 may advance from operation 602 to operation 608when operation 602 is satisfied. In such embodiments, operation 608 maybe operative to identify each moment that is not only (i) associatedwith the particular interest but also (ii) associated with the selectedperson identity (e.g., generally associated with the selected personidentity without any condition with respect to any location (e.g., alocation that may be intimately associated with the selected personidentity) or with respect to any threshold number of such moments) and,in some embodiments, also (iii) not associated with any blacklistelement of the particular interest.

Operation 608 may be omitted in some embodiments of process 600, wherebyprocess 600 may advance from operation 606 to operation 610 whenoperation 606 is satisfied. Additionally, in some embodiments,operations 606 and 608 may be omitted in some embodiments of process600, whereby process 600 may advance from operation 604 to operation 610when operation 604 is satisfied. In either of such embodiments,operation 610 may be operative to determine whether a moment that may beassociated with the particular interest and any location intimatelyassociated with the selected person identity is a valuable interestmoment when each MCI of at least a threshold percentage of the totalnumber of MCIs associated with the moment is (i) associated with theparticular interest and, in some embodiments, also (iii) not associatedwith any blacklist element of the particular interest. In suchembodiments, operation 610 may be operative to use each moment that maybe associated with the particular interest and any location intimatelyassociated with the selected person identity as an identified moment forthe purposes of defining a valuable interest moment at operation 610,such that any valuable interest moment identified at operation 610 wouldbe associated with the particular interest and any location intimatelyassociated with the selected person identity (e.g., as opposed to amoment that might not be associated with such a location intimatelyassociated with the selected person identity). Alternatively, in someembodiments, operation 608 may be included in process 600 but operation610 may be operative to also define each moment that may be associatedwith the particular interest and any location intimately associated withthe selected person identity as a valuable interest moment, such thatoperation 612 may be operative to identify each MCI associated with theparticular interest from each moment that is associated with theparticular interest and with any location intimately associated with theselected person identity.

In some embodiments, any suitable operation(s) of process 600 may beoperative to define any suitable “person identity interest” metadatathat may be associated with one or more MCIs and/or one or more momentsand/or one or more nodes of network 107 n. For example, each MCIidentified at operation 612 or each MCI used to define a collection atoperation 616 may be associated with “person identity interest” metadatathat may be indicative of the particular interest determined to berelevant to the selected person identity may be associated with thatMCI. A “person identity interest” node associated with such “personidentity interest” metadata may be added to metadata network 107 n andmay be correlated to the person identity node for the selected personidentity (e.g., to person identity node 426) and/or may be correlated tothe particular interest node for the particular interest (e.g., to scenedog node 436) and/or may be correlated to each moment node associatedwith each one of the person identity and particular interest (e.g.,moment nodes 402 and 404) and/or associated with each MCI identified atoperation 612 and/or operation 616, and/or the like. Such “personidentity interest” metadata and/or node may be used to enable faster MCIcollection definition of a large media library and/or may be operativeto enable presentation of such identified person identity interests(e.g., in any suitable UI to a user, which may enable the user torequest a collection of MCIs associated with a selected person identityinterest).

System 1 may be operative to manage a media library in order to define acollection of media content items of a media library for a relevantinterest, where such a collection of MCIs may be presented to a user inany suitable manner. For example, as shown in FIG. 5 , system 1 mayinclude a media management system 500 that may be provided to managemedia of media library 105 of system 1, such as, for example, to (i)generate metadata network 107 n, (ii) relate and/or present at least twoMCIs 310 based on metadata network 107 n, (iii) determine and/or presentinteresting MCIs 310 of library 105 based on metadata network 107 n andpredetermined criterion, (iv) select and/or present representative MCIs310 to summarize a collection (e.g., a moment) of media content itemsbased on input specifying the representative group's size, and/or (v)use metadata network 107 n and/or any other data associated with library105 to define a collection of media content items of a media library fora relevant interest. Media management system 500 may include anysuitable modules, including, but not limited to, MCP management system107, which may include metadata network 107 n, a collection generator505, a layout generator 510, a context identifier 515, a scoring engine520, a media compositor or collection enhancer 575, which may include avideo compositor 525 and/or an audio compositor 530, and a renderingengine 535. As shown, media management system 500 may have access tomedia library 105 of MCPs 305, metadata network 107 n of MCP managementsystem 107, media grouping templates 545, media collections 550, anaudio library 555, a video presentation storage 560, and/or an audiopresentation storage 565.

Media management system 500 may be operative to provide amedia-compositing application (e.g., an application 103) that mayautomatically organize MCIs 310 of library 105 into different MCIcollections and/or may enable a user to define MCI collections in anysuitable manner, and that may then produce any suitable user interface(“UI”) layout that may identify the defined MCI collections ascollections that may be viewed by a user (e.g., as individual MCIs of aselected collection) or for which the application may display compositepresentations (e.g., video (e.g., audio/visual) presentations for aselected collection). Media management system 500 may enable such amedia-compositing application that may be executed by system 1.Collection generator 505 and layout generator 510 may be operative toperform an automated process that may (i) analyze the MCIs (e.g.,analyzes the MCIs and any associated metadata of library 105, includingmetadata network 107 n and/or any suitable contextual data) to defineone or more MCI collections and (ii) produce a UI layout that mayidentify the defined MCI collections as collections for which theapplication can display composite presentations. In performing suchoperations, these modules may use scoring engine 520 and/or contextidentifier 515. For example, to define the MCI collections, collectiongenerator 505 may use one or more media grouping templates (“templates”)of template storage 545 to try to associate each MCI of library 105 withone or more template instances. In some embodiments, a template intemplate storage 545 may be defined by reference to a set of mediamatching attributes and collection generator 505 may compare atemplate's attribute set with the content and/or metadata of the MCIs inorder to identify MCIs that may match the template attributes, suchthat, when a sufficient number of MCIs match the attribute set of atemplate, the application may define a template instance by reference tothe matching MCIs and may store such a template instance in mediacollection storage 550. In some embodiments, a template instance mayinclude a list of MCI identifiers that identify the MCI's that matchedthe instance's template attribute set. Collection generator 505 may beoperative to define multiple template instances for a template, such aswhere the templates may include (i) location-bounded templates (e.g.,MCIs captured within a region with a particular radius), (ii)time-bounded templates (e.g., MCIs captured within a particular timerange and/or date range), (iii) time-bounded and location-boundedtemplates (e.g., mornings at a beach), (iv) content-defined templates(e.g., MCIs containing a relevant interest (e.g., a dog as describedabove) or MCIs containing smiles, etc.), and (v) user-metadata basedtemplates (e.g., MCIs from albums created by a user, MCIs shared by auser with others, MCIs having particular user-defined metadata tags,etc.).

Based on template definition, layout generator 510 may generate anysuitable UI layouts that may identify one or more defined templateinstances as MCI collections for which the application can displaycomposite presentations. Layout generator 510 may generate a UI layoutthat identifies a subset of defined template instances that may becontextually relevant to a user of the device at a particular time(e.g., as based on any suitable contextual attributes that may beprovided by context identifier 515 and any template instance scores thatmay be computed by scoring engine 520 (e.g., to assess whether onetemplate instance is contextually more relevant than, and/or betterthan, another template instance at a particular time, scoring engine 520may generate a score for each template instance, rank the templateinstances based on the generated scores, and then generate a UI layoutbased on the rankings so that a user may choose a template instance(e.g., MCI collection) for use in presenting a related compositepresentation), where a template instance's score may be based on anysuitable contextual information, such as (i) contextual attributes thatmay relate to the time at which the UI layout is being generated and/ordisplayed, and (ii) quality and/or quantity attributes that may relateto quality and/or quantity of the MCIs of the template instance, whereexamples of contextual attributes may include (i) time, (ii) location ofdevice 100, (iii) location of future calendared events stored on, oraccessible by, device 100, (iv) locations derived from electronictickets stored on device 100, and/or the like).

When a user selects a particular template instance via any suitable UI,or when system 1 may automatically select a particular template instancein any suitable manner, layout generator 510 may direct collectionenhancer 575 to generate media collection enhancement definitions thatmay be rendered by engine 535 to produce an enhancement (e.g., acomposite presentation) for consumption by the user (e.g., via anysuitable UI (e.g., I/O component 109 a)) For example, layout generator510 may be operative to direct video compositor 525 and/or audiocompositor 530 to generate, for the selected template instance, thedefinitions of video and audio presentation components (e.g., forstorage in storage 560 and/or 565), which rendering engine 535 may thenrender to produce a composite presentation for display. Collectionenhancer 575 may generate the definition of the composite mediapresentation from the MCIs of the selected template instance. In someembodiments, a composition presentation may be generated for eachtemplate instance prior to selection of a particular template instance.

In some embodiments, the definition of a composite media presentationmay include the identity of the MCIs of the collection that are to beincluded in the presentation, the presentation order for the includedMCIs, and/or a list of any suitable edit operations (e.g., transitionoperations, special effects, etc.) that may be performed to generate thecomposite presentations from the MCIs. In some embodiments, the MCIs ofthe composite media presentation can be identical to the MCIs of thetemplate instance, or they can be MCIs that the media compositor mayderive from the instance's MCIs. For example, multiple MCIs of atemplate instance can be still photos, where, for some or all of thesestill photos, collection enhancer 575 (e.g., video compositor 525) maygenerate a video clip in the composite generation by specifying aparticular type of effect (e.g., a “Ken Burns” effect) for each of thesephotos. Also, from a video clip MCI of a template instance, theapplication can extract one or more video clips to include in thecomposite presentation. Similarly, from an MCI that is a burst-modesequence, collection enhancer 575 may be operative to extract one ormore still photos of the sequence and/or one or more types of videoclips for one or more of the still photos of the sequence. Many otherexamples of deriving the composite-presentation with MCIs from atemplate instance's MCIs exist.

Collection enhancer 575 may be operative to generate composite mediadefinitions by selecting a blueprint from a number of possibleblueprints for the composite presentation. A blueprint may describe thedesired transitions, effects, edit styles (e.g., including pace of theedits), and/or the like for a composite presentation. A blueprint canalso specify the desired type of presentation, which can then influencethe type of MCIs included or emphasized in the composite presentation.For example, one blueprint might specify highlights as the desired typeof presentation, while another blueprint might specify retrospective asthe desired type. For highlights, collection generator 505 or collectionenhancer 575 may select the best MCIs that are representative of theMCIs of the template instance. For retrospectives, collection generator505 or collection enhancer 575 may select the MCIs that are notnecessarily of the whole set of MCIs of the template instance. In someembodiments, a blueprint may determine the duration of the compositepresentation that collection enhancer 575 may generate. System 500 maybe operative to specify the duration based on the amount ofhigh-quality, unique content in the MCI collection of the templateinstance. For instance, in some embodiments, a blueprint's specifiedparameters (e.g., parameters specifying ideal duration for the MCIs)along with the MCIs that are selected may determine the desired durationof the composite presentation. In some embodiments, the blueprint mightalso specify other suitable parameters.

A type of a relevant particular interest (e.g., a particular interestthat may result in a collection of MCIs being defined by process 600)for an MCI collection of the template instance may be used by collectionenhancer 575 to determine which blueprint of the available blueprints isto be used for defining the composite presentation. For example,continuing with an example referred to above where a “dog” may be aparticular interest that may be identified as relevant so as to be usedto define an MCI collection, that particular interest of “dog” may beutilized by system 500 (e.g., by collection enhancer 575) to determineautomatically the generation of the composite generation in any suitableway(s) (e.g., to choose a particular type of blueprint (e.g., ablueprint associated with a happy mood or a sentimental mood) for a“dog” particular interest and to choose a different type of blueprint(e.g., a blueprint associated with an extreme mood) for a “skydiving”particular interest.

In some embodiments, collection enhancer 575 may be operative to providethe particular duration of time for the composite presentation to audiocompositor 530, after such a duration may have been selected by a userand/or mood and/or keyword and/or blueprint. Based on the receivedduration, audio compositor 530 may dynamically define a composite audiopresentation to accompany the composite media presentation of videocompositor 525. Audio compositor 530 may dynamically define an audiopresentation to include several audio segments (e.g., of one or more ofthe MCIs and/or from audio library 555) in a particular sequence, and aset of edits and transitions between the audio segments in the sequence.In some embodiments, the audio segments are part of one song, while inother embodiments, they can be part of two or more songs. These audiosegments may be referred to as body segments to signify that they areparts of another song. In some embodiments, audio compositor 530 mayalso select an ending segment from several candidate ending segments forthe composite audio presentation. Audio compositor 530 may select astarting segment from several starting segments for the composite audiopresentation. An editor may define a body, starting and ending segmentsfrom one or more songs by using any suitable audio authoring tools.

When an MCI collection is associated with a particular relevantinterest, that interest may be used by audio compositor 530 to determinean appropriate song or collection of songs to be used to generate atleast a portion of an audio portion of composite presentation. In someparticular embodiments, a particular blueprint may be selected based ona particular interest associated with the MCI collection, where theparticular blueprint may be associated with a number of differentpossible songs, and a particular one or more of the different possiblesongs may be selected for the particular blueprint based on theparticular interest associated with the MCI collection. Alternatively,no matter how a blueprint may be selected, one or more songs to be usedfor defining a composite presentation with the selected blueprint may beselected based on one or more valid keywords associated with the MCIcollection. The group of available songs from which collection enhancer575 (e.g., audio compositor 530) may select one or more songs (e.g.,from audio library 555) may be any suitable collection of songs, whichmay be a particular curated list of music for collection enhancer 575 ormay be any suitable song from a library of songs available to system 1(e.g., a user's personal music collection available to system 1). Eachsong may be associated with any suitable metadata that may be used bycollection enhancer 575 in order to select an appropriate one or moresongs based on any valid relevant interest associated with the MCIcollection. For example, lyric metadata and/or song title metadataand/or artist metadata and/or tempo metadata and/or song style metadataand/or the like may be compared with the interest type to identify oneor more songs to use for generating one or more composite presentationsfor the MCI collection. For example, a relevant interest of “dog” may beoperative to enable collection enhancer 575 to identify and select adog-themed song for use with the composite presentation.

After collection enhancer 575 may generate a definition of the compositepresentation (e.g., after video compositor 525 may generate a definitionfor a video/media presentation, and after audio compositor 530 maygenerate a definition for an audio (e.g., song) presentation), thegenerated presentation definition(s) may be stored in storage (e.g.,video/media presentation definition(s) in storage 560 and audio (e.g.,song) presentation definition(s) in storage 565, although in someembodiments one storage (e.g., one file) may be used for both video andaudio definitions). Rendering engine 535 may then retrieve thedefinition(s) and generate a rendered composite presentation from thedefinition(s), which may then be output to a frame buffer of the systemfor presentation. One of ordinary skill will realize that system 500 mayoperate in any other suitable manner, such as, instead of defining acomposite presentation for a template instance after a user selects aparticular instance and/or mood and/or duration, system 500 may definesthe composite presentation for some or all appropriate MCI collectionsin advance of receive any user selection. For instance, in someembodiments, the system may identify a subset of composite presentationsthat should initially be concurrently represented on a UI layout, and toidentify an order of summary panes for those composite presentations onthe UI layout. Alternatively, some embodiments may render the compositepresentations before generating the UI layout, while still otherembodiments may define a portion of a composite presentation before theUI layout is generated, and then may generate the rest of the definitionof the composite presentation after the UI layout is generated.

In some embodiments, after a user selects a particular MCI collection ofa particular template instance, system 500 may direct collectionenhancer 575 to generate, for the selected template instance, thedefinition of the composite presentation. To generate the definition ofthe media composite presentation, collection enhancer 575 mayautomatically pick a mood for the composite presentation based on anysuitable available data, including one or more identified relevantinterests associated with the MCI collection. After picking the mood,collection enhancer 575 may select from a number of different blueprintsa particular blueprint for the composite presentation based on thepicked mood. The blueprint may describe any suitable characteristics ofthe presentation to be generated based on the blueprint, including, butnot limited to, the desired transitions, effects, edit styles (e.g.,including pace of the edits), the desired type of presentation, and/orthe like, where one or more of such characteristics may vary betweendifferent blueprints. Collection enhancer 575 may select a subset or allof the MCIs of the MCI collection based on the picked mood and/or theselected blueprint for use in generating the composite presentation.Collection enhancer 575 may select a particular duration of time for thecomposite presentation based on the picked mood and/or the selectedblueprint and/or the selected MCIs for use in generating the compositepresentation. In conjunction with the selected blueprint, which mayspecify the type of desired edits (e.g., fast transition edits, or slowtransition edits), the selection of the subset of MCIs may allowcollection enhancer 575 to automatically define the duration of thecomposite presentation without any user input. After computing thedesired duration of the composite presentation, collection enhancer 575may provide this duration to audio compositor 530 in order for audiocompositor 530 to dynamically generate the definition of a songpresentation that has this duration. As mentioned above, audiocompositor 530 may be operative to generate this definition by exploringdifferent combinations of body segments from one or more songs availablesongs, along with different possible starting and ending segments, whereone or more songs may be selected from a collection of available songsbased on one or more of any relevant particular interest associated withthe MCI collection, the mood chosen for the presentation, the blueprintchosen for the presentation, and/or the duration of time chosen for thepresentation. Then the video/media/audio presentation definitions may besynched and provided to rendering engine 535 that may generate arendered composite presentation from these definitions.

FIG. 7 is a flowchart of an illustrative process 700 for analyzing mediacontent of a media library (e.g., with a computing system). At operation702 of process 700, the computing system may access a plurality of mediacontent items (MCIs) of a media library and metadata associated with themedia library, wherein the metadata defines a plurality of moments, eachmoment of the plurality of moments is associated with a subset of MCIsof the plurality of MCIs, and each MCI of the subset of MCIs that isassociated with a particular moment is associated with geographicalmetadata indicative of a geographic location within a particulargeographic range associated with the particular moment and temporalmetadata indicative of a time within a particular time range associatedwith the particular moment. At operation 704 of process 700, thecomputing system may analyze the plurality of MCIs and the metadata byidentifying a plurality of first person residence moments from theplurality of moments, wherein each first person residence moment of theplurality of first person residence moments is a moment of the pluralityof moments that is associated with both a first person and a residenceof the first person, and by identifying an interest that is associatedwith each one of a first number of first person residence moments of theplurality of first person residence moments, wherein the first number isgreater than a threshold value. At operation 706 of process 700, thecomputing system may define a collection of MCIs of the plurality ofMCIs, wherein each MCI of the collection of MCIs is associated with amoment of the plurality of moments that is associated with both thefirst person and the interest.

It is understood that the operations shown in process 700 of FIG. 7 areonly illustrative and that existing operations may be modified oromitted, additional operations may be added, and the order of certainoperations may be altered.

FIG. 8 is a flowchart of an illustrative process 800 for managing amedia library with a computing system. At operation 802 of process 800,the computing system may access a plurality of media content items(MCIs) of the media library and metadata associated with the medialibrary, wherein the metadata defines a plurality of moments, eachmoment of the plurality of moments is associated with a subset of MCIsof the plurality of MCIs, and each MCI of the subset of MCIs that isassociated with a particular moment is associated with temporal metadataindicative of a time within a particular time range associated with theparticular moment. At operation 804 of process 800, the computing systemmay analyze the plurality of MCIs and the metadata by identifying atleast one person residence moment from the plurality of moments, whereineach person residence moment of the at least one person residence momentis a moment of the plurality of moments that is associated with alocation intimately associated with a person identity, and byidentifying an interest that is associated with at least one of the atleast one person residence moment. At operation 806 of process 800, thecomputing system may define a collection of MCIs of the plurality ofMCIs, wherein each MCI of the collection of MCIs is associated with theidentified interest.

It is understood that the operations shown in process 800 of FIG. 8 areonly illustrative and that existing operations may be modified oromitted, additional operations may be added, and the order of certainoperations may be altered.

FIG. 9 is a flowchart of an illustrative process 900 for managing amedia library with a computing system. At operation 902 of process 900,the computing system may access a plurality of media content items(MCIs) of the media library. At operation 904 of process 900, thecomputing system may identify, with the computing system, at least athreshold number of MCIs of the plurality of MCIs that are associatedwith both a particular person entity and a particular interest. Atoperation 906 of process 900, the computing system may define acollection of MCIs of the plurality of MCIs, wherein each MCI of thecollection of MCIs is associated with the particular interest.

It is understood that the operations shown in process 900 of FIG. 9 areonly illustrative and that existing operations may be modified oromitted, additional operations may be added, and the order of certainoperations may be altered.

Moreover, one, some, or all of the processes described with respect toFIGS. 1-9 may each be implemented by software, but may also beimplemented in hardware, firmware, or any combination of software,hardware, and firmware. They each may also be embodied as machine- orcomputer-readable code recorded on a machine- or computer-readablemedium. The computer-readable medium may be any data storage device thatcan store data or instructions which can thereafter be read by acomputer system. Examples of such a non-transitory computer-readablemedium (e.g., memory 104 of FIG. 1 ) may include, but are not limitedto, read-only memory, random-access memory, flash memory, CD-ROMs, DVDs,magnetic tape, removable memory cards, optical data storage devices, andthe like. The computer-readable medium can also be distributed overnetwork-coupled computer systems so that the computer-readable code isstored and executed in a distributed fashion. For example, thecomputer-readable medium may be communicated from one electronic deviceto another electronic device using any suitable communications protocol(e.g., the computer-readable medium may be communicated to electronicdevice 100 via any suitable communications circuitry 114 (e.g., as atleast a portion of application 103)). Such a transitorycomputer-readable medium may embody computer-readable code,instructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A modulateddata signal may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.

It is to be understood that any or each module of media managementsystem 500 may be provided as a software construct, firmware construct,one or more hardware components, or a combination thereof. For example,any or each module of media management system 500 may be described inthe general context of computer-executable instructions, such as programmodules, that may be executed by one or more computers or other devices.Generally, a program module may include one or more routines, programs,objects, components, and/or data structures that may perform one or moreparticular tasks or that may implement one or more particular abstractdata types. It is also to be understood that the number, configuration,functionality, and interconnection of the modules of media managementsystem 500 are only illustrative, and that the number, configuration,functionality, and interconnection of existing modules may be modifiedor omitted, additional modules may be added, and the interconnection ofcertain modules may be altered.

At least a portion of one or more of the modules of media managementsystem 500 may be stored in or otherwise accessible to device 100 in anysuitable manner (e.g., in memory 104 of device 100 (e.g., as at least aportion of application 103)) and/or to server 50. Any or each module ofmedia management system 500 may be implemented using any suitabletechnologies (e.g., as one or more integrated circuit devices), anddifferent modules may or may not be identical in structure,capabilities, and operation. Any or all of the modules or othercomponents of media management system 500 may be mounted on an expansioncard, mounted directly on a system motherboard, or integrated into asystem chipset component (e.g., into a “north bridge” chip).

Any or each module of media management system 500 may be a dedicatedsystem implemented using one or more expansion cards adapted for variousbus standards. For example, all of the modules may be mounted ondifferent interconnected expansion cards or all of the modules may bemounted on one expansion card. With respect to media management system500, by way of example only, the modules of media management system 500may interface with a motherboard or processor 102 of device 100 throughan expansion slot (e.g., a peripheral component interconnect (“PCI”)slot or a PCI express slot). Alternatively, media management system 500need not be removable but may include one or more dedicated modules thatmay include memory (e.g., RAM) dedicated to the utilization of themodule. In other embodiments, media management system 500 may be atleast partially integrated into device 100. For example, a module ofmedia management system 500 may utilize a portion of device memory 104of device 100. Any or each module of media management system 500 mayinclude its own processing circuitry and/or memory. Alternatively, anyor each module of media management system 500 may share processingcircuitry and/or memory with any other module of media management system500 and/or processor 102 and/or memory 104 of device 100. Alternatively,any or each module of media management system 500 may share processingcircuitry and/or memory of server 50 remote from device 100.

As described above, one aspect of the present technology is thegathering and use of data available from various sources to define acollection of media content items of a media library for a relevantinterest. The present disclosure contemplates that in some instances,this gathered data may include personal information data that uniquelyidentifies or can be used to contact or locate a specific person. Suchpersonal information data can include demographic data, location-baseddata, telephone numbers, email addresses, social network identifiers,home addresses, office addresses, data or records relating to a user'shealth or level of fitness (e.g., vital signs measurements, medicationinformation, exercise information, etc.), date of birth, or any otheridentifying or personal information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used toimprove the definition of a collection of media content items of a medialibrary for a relevant interest with the electronic device. Further,other uses for personal information data that benefit the user are alsocontemplated by the present disclosure. For instance, health and fitnessdata may be used to provide insights into a user's general wellness, ormay be used as positive feedback to individuals using technology topursue wellness goals.

The present disclosure contemplates that the entities responsible forthe collection, analysis, disclosure, transfer, storage, or other use ofsuch personal information data will comply with well-established privacypolicies and/or privacy practices. In particular, such entities shouldimplement and consistently use privacy policies and practices that aregenerally recognized as meeting or exceeding industry or governmentalrequirements for maintaining personal information data private andsecure. Such policies should be easily accessible by users, and shouldbe updated as the collection and/or use of data changes. Personalinformation from users should be collected for legitimate and reasonableuses of the entity and not shared or sold outside of those legitimateuses. Further, such collection/sharing should occur after receiving theinformed consent of the users. Additionally, such entities shouldconsider taking any needed steps for safeguarding and securing access tosuch personal information data and ensuring that others with access tothe personal information data adhere to their privacy policies andprocedures. Further, such entities can subject themselves to evaluationby third parties to certify their adherence to widely accepted privacypolicies and practices. In addition, policies and practices should beadapted for the particular types of personal information data beingcollected and/or accessed and adapted to applicable laws and standards,including jurisdiction-specific considerations. For instance, in theUnited States, collection of or access to certain health data may begoverned by federal and/or state laws, such as the Health InsurancePortability and Accountability Act (“HIPAA”); whereas health data inother countries may be subject to other regulations and policies andshould be handled accordingly. Hence different privacy practices shouldbe maintained for different personal data types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof location detection services, the present technology can be configuredto allow users to select to “opt in” or “opt out” of participation inthe collection of personal information data during registration forservices or anytime thereafter. In addition to providing “opt in” or“opt out” options, the present disclosure contemplates providingnotifications relating to the access or use of personal information. Forinstance, a user may be notified upon downloading an app that theirpersonal information data will be accessed and then reminded again justbefore personal information data is accessed by the app.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data a city level rather than at an address level),controlling how data is stored (e.g., aggregating data across users),and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, the definitionof a collection of media content items of a media library for a relevantinterest can be made based on non-personal information data or a bareminimum amount of personal information, such as the content beingrequested by the device associated with a user, other non-personalinformation available to the device, or publicly available information.

While there have been described systems, methods, and computer-readablemedia for defining a collection of media content items of a medialibrary for a relevant interest, it is to be understood that manychanges may be made therein without departing from the spirit and scopeof the subject matter described herein in any way. Insubstantial changesfrom the claimed subject matter as viewed by a person with ordinaryskill in the art, now known or later devised, are expressly contemplatedas being equivalently within the scope of the claims. Therefore, obvioussubstitutions now or later known to one with ordinary skill in the artare defined to be within the scope of the defined elements.

Therefore, those skilled in the art will appreciate that the inventioncan be practiced by other than the described embodiments, which arepresented for purposes of illustration rather than of limitation.

What is claimed is:
 1. A method for producing composite presentations ofmedia items, the method comprising, at a computing device: selecting afirst type of metadata associated with at least one interest; selectinga second type of metadata associated with at least one moment;identifying a plurality of media items within a media library, whereineach media item of the plurality of media items is tagged with the firstand second types of metadata; and in response to identifying that anumber of media items included in the plurality of media items satisfiesa threshold for producing a composite presentation of media items:forming a collection of media items based on at least some of the mediaitems of the plurality of media items, selecting, based on the firstand/or second type of metadata, a template for producing the compositepresentation, and producing the composite presentation based on thetemplate and at least some of the media items included in the collectionof media items.
 2. The method of claim 1, wherein the at least oneinterest is associated with at least one of one or more persons, ahobby, a pet, an instrument, and an activity.
 3. The method of claim 1,wherein the at least one moment is associated with at least an eventthat occurs over a respective period of time at a respective geographiclocation.
 4. The method of claim 3, wherein the geographic locationincludes at least one of a residence, a frequently visited location, anda vacation locale.
 5. The method of claim 1, wherein the at least someof the media items included in the collection of media items areselected based on a desired length of the composite presentation.
 6. Themethod of claim 1, wherein the template defines a plurality ofcharacteristics for the composite presentation to exhibit.
 7. The methodof claim 1, wherein the first and second types of metadata are definedin a grouping template.
 8. A non-transitory computer readable storagemedium configured to store instructions that, when executed by aprocessor included in a computing device, cause the computing device toproduce composite presentations of media items, by carrying out stepsthat include: selecting a first type of metadata associated with atleast one interest; selecting a second type of metadata associated withat least one moment; identifying a plurality of media items within amedia library, wherein each media item of the plurality of media itemsis tagged with the first and second types of metadata; and in responseto identifying that a number of media items included in the plurality ofmedia items satisfies a threshold for producing a composite presentationof media items: forming a collection of media items based on at leastsome of the media items of the plurality of media items, selecting,based on the first and/or second type of metadata, a template forproducing the composite presentation, and producing the compositepresentation based on the template and at least some of the media itemsincluded in the collection of media items.
 9. The non-transitorycomputer readable storage medium of claim 8, wherein the at least oneinterest is associated with at least one of one or more persons, ahobby, a pet, an instrument, and an activity.
 10. The non-transitorycomputer readable storage medium of claim 8, wherein the at least onemoment is associated with at least an event that occurs over arespective period of time at a respective geographic location.
 11. Thenon-transitory computer readable storage medium of claim 10, wherein thegeographic location includes at least one of a residence, a frequentlyvisited location, and a vacation locale.
 12. The non-transitory computerreadable storage medium of claim 8, wherein the at least some of themedia items included in the collection of media items are selected basedon a desired length of the composite presentation.
 13. Thenon-transitory computer readable storage medium of claim 8, wherein thetemplate defines a plurality of characteristics for the compositepresentation to exhibit.
 14. The non-transitory computer readablestorage medium of claim 8, wherein the first and second types ofmetadata are defined in a grouping template.
 15. A computing deviceconfigured to produce composite presentations of media items, thecomputing device comprising a processor configured to cause thecomputing device to carry out steps that include: selecting a first typeof metadata associated with at least one interest; selecting a secondtype of metadata associated with at least one moment; identifying aplurality of media items within a media library, wherein each media itemof the plurality of media items is tagged with the first and secondtypes of metadata; and in response to identifying that a number of mediaitems included in the plurality of media items satisfies a threshold forproducing a composite presentation of media items: forming a collectionof media items based on at least some of the media items of theplurality of media items, selecting, based on the first and/or secondtype of metadata, a template for producing the composite presentation,and producing the composite presentation based on the template and atleast some of the media items included in the collection of media items.16. The computing device of claim 15, wherein the at least one interestis associated with at least one of one or more persons, a hobby, a pet,an instrument, and an activity.
 17. The computing device of claim 15,wherein the at least one moment is associated with at least an eventthat occurs over a respective period of time at a respective geographiclocation.
 18. The computing device of claim 17, wherein the geographiclocation includes at least one of a residence, a frequently visitedlocation, and a vacation locale.
 19. The computing device of claim 15,wherein the at least some of the media items included in the collectionof media items are selected based on a desired length of the compositepresentation.
 20. The computing device of claim 15, wherein the templatedefines a plurality of characteristics for the composite presentation toexhibit.